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Process planning for precision manufacturing

An approach based on methodological

studies

Mats Bagge

Doctoral Thesis 2014

KTH Royal Institute of Technology

Engineering Sciences

Department of Production Engineering

SE-100 44 Stockholm, Sweden

TRITA IIP-14-04

ISSN 1650-1888

ISBN 978-91-7595-172-0

Akademisk avhandling som med tillstånd av KTH i Stockholm framlägges till offentlig

granskning för avläggande av teknisk doktorsexamen tisdagen den 10 juni 2014 kl. 13:00 i

sal M311, KTH Industriell Produktion, Brinellvägen 68, Stockholm

Copyright © Mats Bagge 2014

Tryck: Universitetsservice US AB

III

Abstract Process planning is a task comprising a broad range of activities to

design and develop an appropriate manufacturing process for producing

a part. Interpretation of the part design, selection of manufacturing

processes, definition of operations, operation sequences, machining

datums, geometrical dimensions and tolerances are some common

activities associated with the task. Process planning is also “the link

between product design and manufacturing” with the supplementary

commission to support design of competitive products.

Process planning is of a complex and dynamic nature, often managed

by a skilled person with few, or no, explicit methods to solve the task. The

work is heuristic and the result is depending on personal experiences and

decisions. Since decades, there have been plenty of attempts to develop

systems for computer-aided process planning (CAPP). CAPP is still

awaiting its breakthrough and one reason is the gap between the

functionality of the CAPP systems and the industrial process planning

practice.

This thesis has an all-embracing aim of finding methods that cover

essential activities for process planning, including abilities to predict the

outcome of a proposed manufacturing process. This is realised by

gathering supporting methods suitable to manage both qualitative and

quantitative characterisation and analyses of a manufacturing process.

The production research community has requested systematisation

and deeper understanding of industrial process planning. This thesis

contributes with a flow chart describing the process planning process

(PPP), in consequence of the methodological studies. The flow chart

includes process planning activities and information flows between these

activities.

The research has been performed in an industrial environment for

high volume manufacturing of gear parts. Though gear manufacturing

has many distinctive features, the methods and results presented in this

thesis are generally applicable to precision manufacturing of many kinds

of mechanical parts.

Keywords

Process planning, precision manufacturing, machining, tolerance

chain analysis, process behaviour, process performance, process

capability, in-process workpiece.

V

Acknowledgements

This thesis is a result of my work as an industrial PhD student at the

Department of Production Engineering IIP, KTH Royal Institute of

Technology, employed by Scania CV AB, Södertälje. Since the start in

2006, I have been involved in several research projects where

collaborations between Swedish industry, academia and research

institutes have been a common denominator. These projects have

provided many interesting research topics related to my main field of

research – process planning – and given many fruitful opportunities to

get to know nice and proficient people.

Among these people, I would first like to express my special

appreciation and thanks to my supervisors Professor Bengt Lindberg and

Professor Sören Andersson at KTH and Ulf Bjarre at Scania CV AB for

inspiring cooperation over the years of studies. The appended

publications in this thesis have also been written in an invigorating

manner together with Professor Cornel Mihai Nicolescu, Mats Werke and

Mikael Hedlind. Thank you!

I would also like to thank my colleague and participant in the KUGG

project, Julia Lundberg Gerth for taking time and giving well advises

when writing this thesis. My appreciations to the other Ph.D. students in

the KUGG project (where it all begun); Mathias Werner, Ellen Bergseth,

Sören Sjöberg and Karin Björkeborn.

One important source for inspiration and discussions about cross-

disciplinary considerations related to production research has been

participating in the seminars lead by Associate Professor Peter Gröndahl.

Many persons have contributed to the engaging enthusiasm in the

seminar group, especially Robert Gerth, Tord Johansson, Joakim Storck,

Jens von Axelson, Richard Lindqvist and Magnus Lundgren.

I have in addition had the favour to get to know and spend time

together with many people at IIP, in front of others; Anders Berglund,

Andreas Archenti and Lorenzo Daghini. It has been a pleasure to cadge

your offices!

- Marie, Johan, Maj-Britt and Mona, let me express my sincere

gratitude for all your assistance when colloquial duties have not fit into

my business schedule!

Finally, all my love goes to Carolin, Hanna and Ellen. You are great!

Mats Bagge, Södertälje, May 2014

VII

List of publications

This thesis is based on five publications of which one is a licentiate

thesis and four are papers. The four papers are appended at the end of

this doctoral thesis.

Licentiate thesis (not appended)

Bagge, M., 2009. An approach for systematic process planning of gear

transmission parts. Licentiate thesis, Stockholm, Sweden: Royal Institute

of Technology.

Paper A

Werke, M., Bagge, M., Nicolescu, M. & Lindberg, B., 2014. Process

modelling using upstream analysis of manufacturing sequences.

Submitted to: The International Journal of Advanced Manufacturing

Technology (Under review April 2014).

Paper B

Bagge, M. & Lindberg, B., 2012. Analysis of process parameters during

press quenching of bevel gear parts. In: M. Björkman, ed. Proceedings of

The 5th International Swedish Production Symposium. Linköping:

Produktionsakademien, pp. 251-259.

Paper C

Bagge, M., Hedlind, M. & Lindberg, B., 2013. Tolerance chain design and

analysis of in-process workpiece. In: A. Archenti & A. Maffei, eds.

Proceedings of the International Conference on Advanced

Manufacturing Engineering and Technologies. Stockholm: KTH Royal

Institute of Technology, pp. 305-315.

Paper D

Bagge, M., Hedlind, M. & Lindberg, B., 2014. Process chain based

workpiece variation simulation for performance utilisation analysis.

Submitted to: Swedish Production Symposium 2014, Göteborg, Sweden.

IX

Abbreviations

AI: Artificial Intelligence

AIAG: Automotive Industry Action Group

APQP: Advanced Product Quality

CAD: Computer Aided Design

CAM: Computer Aided Manufacturing

CAPP: Computer Aided Process Planning

CoPQ: Cost of Poor Quality

DDC: Dimension Dependency Chart

DDC2: Dimension Dependency Chart 2

DOE: Design of Experiments

DPMO: Defects per Million Opportunities

FD: Final Dimension

FFI: Fordonsstrategisk Forskning och Innovation

Icam: Integrated Computer Aided Manufacturing

IDEF: Integration DEFfinition

IPW: In Process Workpiece

KPI: Key Performance Index

NC: Numerical Control

OE: Operation Element

OEM: Original Equipment Manufacturer

PCI: Process Capability Index

PLM: Product Lifecycle Management

PPAP: Production Part Approval Process

P-FMEA: Process Failure Mode and Effects Analysis

PPM: Parts Per Million

PPP: Process Planning Process

PUR: Performance Utilisation Ratio

R&D: Research and Development

RE: Random Error

ReVer: Realistic Verification

SE: Systematic Error

T1, T2... : Tool number 1, 2 et cetera

TE: Total Error

VW: Virtual Workshop

XI

Contents

List of publications...................................................................................VII

Abbreviations............................................................................................IX

1 Introduction ..................................................................... 1

1.1 Process planning for manufacturing ............................................... 1

1.2 Structure of the thesis ..................................................................... 4

2 Frame of reference .......................................................... 5

2.1 PPP - the process planning process ............................................... 5

2.2 Process capability – a boundary condition for process planning .... 7

2.3 Conceptions of process characterisation ........................................ 8

2.4 The beam balance .......................................................................... 8

2.5 The beam balance versus process capability ................................. 9

2.6 Transmission part manufacturing .................................................. 12

2.6.1 The transmission parts .......................................................... 12

2.6.2 The workshop ........................................................................ 12

2.6.3 The process planning ............................................................ 13

3 Problems to solve and research objectives ............... 15

3.1 Industrial problem to solve ............................................................ 15

3.2 Related process planning research .............................................. 16

3.3 Research objectives ...................................................................... 20

3.4 Delimitations .................................................................................. 20

4 Research approach ....................................................... 21

4.1 R&D of systematic process planning methods ............................. 21

4.2 Positioning of appended publications ........................................... 24

XII

5 Performed research and synthesis of results ............ 25

5.1 Schematic process planning ......................................................... 26

5.1.1 An approach for systematic process planning of gear

transmission parts .................................................................. 27

5.2 Capture of process behaviour ....................................................... 28

5.2.1 Paper A: Process modelling using upstream analysis of

manufacturing sequences ...................................................... 30

5.2.2 Paper B: Analysis of process parameters during press

quenching of bevel gear parts ............................................... 33

5.2.3 Multivariate data analysis ...................................................... 38

5.3 Tolerance synthesis and analysis ................................................. 39

5.3.1 Paper C: Tolerance chain design and analysis of in-process

workpiece ............................................................................... 41

5.3.2 Paper D: Process chain based workpiece variation simulation

for performance utilisation analysis ....................................... 44

5.4 Synthesis of results ....................................................................... 49

5.4.1 Information flows .................................................................... 49

5.4.2 Refining the PPP with information flows ................................ 52

6 Discussion and conclusions ........................................ 55

6.1 Coverage of the research results .................................................. 55

6.2 Follow-up of research objective 1 .................................................. 56

6.3 Follow-up of research objective 2 .................................................. 58

6.4 The relation between the PPP and PPAP ..................................... 58

6.5 Process capability index ................................................................ 59

6.6 Relevance of the chosen problems to solve ................................. 61

6.7 Scientific contribution ..................................................................... 62

XIII

6.8 General application of the research results .................................. 62

6.9 Conclusions ................................................................................... 63

7 Future work.................................................................... 65

7.1 Economic aspects ......................................................................... 65

7.2 Representation of the PPP and information flows ........................ 65

7.3 The model driven approach .......................................................... 66

7.4 A new approach for P-FMEA ........................................................ 66

8 References ..................................................................... 67

1

1 Introduction

1.1 Process planning for manufacturing

Process planning for manufacturing covers a wide range of activities

needed to specify the manufacturing process for a part. Unfortunately,

the process planning terminology “is somewhat fuzzy” (Anderberg, 2012)

and there is no commonly adopted or standardised definition of what is

included or not, nor for the activities whatever they may be.

Marri, et al. (1998) gives a definition that seems to be applicable in

most cases where process planning is considered:

“Process planning is defined as the activity of deciding which

manufacturing processes and machines should be used to perform the

various operations necessary to produce a component, and the sequence

that the processes should follow.”

A more comprehensive definition of the aim of process planning for

manufacturing is that by ElMaraghy and Nassehi (2013):

“Process planning, in the manufacturing context, is the determination

of processes and resources needed for completing any of the

manufacturing processes required for converting raw materials into a

final product to satisfy the design requirements and intent and respect

the geometric and technological constraints.”

An important addition to these constraints is that the manufacturing

must be cost-effective for a business to be successful. This is realised by a

well-running manufacturing process in addition to efficient process

planning to meet agreed deadlines (Scallan, 2003). The integration of

manufacturing cost is essential and has been discussed by many authors

(Gupta, et al., 2011; Xu, et al., 2009; Mayer & Nusswald, 2001).

Process planning is also described to be the interface between product

design and manufacturing (Scallan, 2003) and the work often includes

coordination of product design intentions and constraints imposed by the

workshop (Ham, 1988). The process planner therefore plays a desirable

role not only to define a process plan but to contribute with

manufacturing knowledge to a competitive product design (Bagge, 2009;

Groche, et al., 2012; Inman, et al., 2013).

To bring some clarity in different focuses for process planning,

ElMaraghy and Nassehi (2013) have defined four process planning levels

http://www.springerreference.com/docs/link/2350785.html?s=349955&t=Process+planning

2 | INTRODUCTION

according to Table 1. These levels are placed in order from a very low level

of detail to a very detailed level. In addition, the focus and output from

each level are identified.

Table 1: Process planning levels (ElMaraghy & Nassehi, 2013).

Process planning

level

Main focus of planning at

this level

Level of detail Planning output at this level

Generic planning

Selecting technology and rapid process planning

Very low Manufacturing technologies and processes, conceptual plans, and DFx analysis results

Macro planning Multi-domain Low Routings, nonlinear plans, alternate resources

Detailed planning

Single domain, single process

Detailed Detailed process plans (sequence, tools, resources, fixtures, etc.)

Micro planning Optimal conditions and machine instructions

Very detailed Process/Operation parameters, time, cost, etc., NC codes

This definition of process planning levels is established in the

academic research community but is not so well known by process

planners in industry. The probable reason is that in practice these four

levels overlap each other and are difficult to distinguish. More easily

recognised are the main activities related to process planning. These

activities have been listed by Alting and Zhang (1989) in ten steps:

1. Interpretation of product design data (CAD data, material properties,

batch size, tolerances, surface definition, heat treatment and hardness,

special requirements).

2. Selection of machining processes (e.g. turning milling, drilling and

grinding), usually based on a company specific strategy.

3. Selection of machine tools considering for example availability,

process capability, machining range and production rate.

4. Determination of fixtures and datum surfaces.

5. Sequencing the operations.

6. Selection of inspection devices.

7. Determination of production tolerances.

INTRODUCTION | 3

8. Determination of the proper cutting tools and conditions (e.g. depth of

cut, feed rate and cutting speed).

9. Calculation of the overall times (machining, non-machining and set-

up times).

10. Generation of process sheets, operation sheets and NC data.

There is no established definition of what a process plan is, and what

process planning work shall include, furthermore not all process planners

in all companies may perform all of the activities.

Process planning has been, and still is, mainly a knowledge intensive

manual task performed by a skilled person because of its multi-

perspective problem nature including “problem-solving, constraint-

reasoning, goal-achieving, resource utilisation and conflict-resolution”

(Ham, 1988). Although computer aided process planning (CAPP) has

been on the production research agenda for decades it has not been

commonly assimilated into industry (Xu, et al., 2011; Anderberg, 2012).

In the end, the goal for the process planner is to design a

manufacturing process that is capable of producing a part that fulfils all

design requirements, without incurring high economic costs. Xu, et al.,

(2009) emphasise the major impact that process planning has on factory

activities and the resulting manufacturing costs.

Process planning is, and will be, an important issue for all

manufacturing companies, no matter whether manual, computer-aided

or, most probably, a combination.

4 | INTRODUCTION

1.2 Structure of the thesis

Figure 1: Structure of the thesis.

5. Performed research and synthesis of results

1. Introduction-A common description of process planning for manufacturing

2. Frame of reference-Process planning as considered in this thesis

4. Research approach

6. Discussion and conclusions

Refined PPP

Schematic

process

plan

Schematic process planning

Tolerancing

Process

control

strategy

Defining process control strategy

Predicted

outcome

of process

Tolerance

analysis

Process

plan

concept

Initial

process

planning

YES

NO

Good

enough?D

Simulations

Design of

Experiments

Multivariate

data

analysis

Work instructions

Tool layout

NC-programs

Process

plan!

Establish

process

plan

Examples of supporting

processes to create:

Measuring programs

Assignment

directive

Control documents

F G

...

Part design

Workshop

Collaboration parties:

Manufacturing technology

BE

I

H

H

H

K

J

I

DC

A

In-process

tolerances

In-process

dimensions

Process behaviour

In-process tolerances

Product characteristics

Process settings

G

F

Part design

K

Framework of process

planning methodsLicentiate thesis

Paper A

Paper B

Paper C and D

Objective 1

Objective 2

3. Problems to solve and research objectives

3.3 Research objectives

3.2 Related process planning research

3.1 Industrial problem to solve

7. Future work

PPP

5

2 Frame of reference

The overall scope of this thesis comprises all levels of process planning

described in Table 1 but does not deal in detail with all potential topics

and activities. The following will be an introduction to the frame of

reference in which the research has been performed and to the origin of

and ideas for resolving the subsequently identified problems.

2.1 PPP - the process planning process

To clarify the general conception of process planning it is appropriate

to make some more statements and explain the desired goal for the

process planner.

Process planning is work that includes all needed activities to define a

process plan. A process plan is a specification that contains information

aimed to facilitate production of a certain part in a definite

manufacturing system. In practice, two tacit boundary conditions narrow

this definition; the process plan must be defined in a way that ensures

that the produced part fulfils all requirements stated in the design

specification as well as minimising the production cost.

As process planning in general is a very broad topic it is here delimited

to fit the purpose of this thesis. My approach to giving an overview of the

process planning domain is shown in Figure 2 as a flow chart of the

“process planning process” (PPP). The PPP flow chart includes activities

derived from my own experiences and ideas; many of them, especially

sub-activities, are also found among the ten activities listed by Alting and

Zhang (1989).

6 | FRAME OF REFERENCE

Figure 2: The PPP flow chart – an approach to describe the process planning

process (PPP).

According to Figure 2, the main flow starts with the activity “initial

process planning” on receiving an assignment directive. The result is a

process plan concept where the main directions of the following work

have been set. This is used together with knowledge about the behaviour

of relevant manufacturing processes as input to the three following

process planning activities.

These three activities are interdependent and are performed in an

iterative way starting with schematic process planning. Schematic

process planning includes, for example, interpretation of design

requirements, definition of production and operation sequence, machine

tools, cutting and work holding tools.

Schematic

process

plan


In-process

work piece

tolerancesTolerancing

Process

control

strategy


ResultTolerance

analysis

Process

plan

concept

Initial

process

planning

YES

NO

Good

enough?

Process

behaviourSimulations

Process

behaviour

Design of

Experiments

Process

behaviour

Multivariate

data

analysis

Known

process

behaviour

Work instructions

Tool layout

NC-programs

Process

plan!

Establish

process

plan



Measuring programs

Control documents

...

Assignment

directive

Part designPart design

FRAME OF REFERENCE | 7

Tolerancing is to define in-process workpiece (IPW) tolerances in

conjunction with the schematic process plan, process behaviour and

design specification of the part.

The process control strategy is based on both the schematic process

plan and the required tolerances, but also on the behaviour of all included

processes.

The defined process plan is then analysed regarding tolerances and the

expected outcome of the process. The results are tested against

acceptance criteria to decide whether the defined manufacturing process

can be approved or not. If the manufacturing process has all the

necessary qualities to succeed, the process plan can be established and all

needed documents, programs, working instructions, etc. created.

2.2 Process capability – a boundary condition for process planning

Because a manufacturing process that produces virtually no products

out of specification tends to be very expensive, there is often an

acceptance criterion allowing a small amount of deviation from

specification. This criterion is commonly defined as the lowest acceptable

level of a process capability index 1 (PCI), the highest number of defect

parts per million (PPM) or sometimes as maximum defects per million

opportunities (DPMO) (Wu, et al., 2009). The aim of using this kind of

criteria is to prohibit poor production process performance in relation to

the requirement, preventing adherent high quality deficiency costs.

PCIs is often used to set a limit for the lowest acceptable process

capability, but has not been used for limiting the highest acceptable

process capability. The aim of a higher limit should be to prevent

excessive manufacturing cost due to overqualified manufacturing

resources; this is in contrast to the commonly used lower limit which

aims to save the customer from getting a product out of specification.

Even though a useful higher limit of capability index is not defined in the

literature, the assumption here is that the process planner must try to

obtain a balance between required product performance and the

economic efforts put into the manufacturing process.

1 Capability index (typically Cp, Cpk, Cm, or Cmk) represents the relation between the requirement

(tolerance range) and the statistically defined process behaviour (e.g. 6*standard deviation).

(Montgomery, 2009)


2.3 Conceptions of process characterisation

Until now, three conceptions of a similar kind have been used to

characterise a process; process performance, process behaviour and

process capability. Some clarifications are in order to avoid confusions.

“Process performance” refers to the accuracy of the process when

creating a final part dimension or an IPW dimension.

“Process behaviour” is a representation of the manner, or action, of

the manufacturing process.

“Process capability” is dedicated to the established conception of PCI

where a tolerance range is related to a statistical representation of the

process behaviour.

2.4 The beam balance

My conceptual approach to show the effect of the relation between

product performance and production performance is illustrated by the

beam balance in Figure 3

The leftmost scale pan contains a load that represents the product

performance, stated as requirements in the design specification. The

rightmost pan contains the load of the manufacturing process

performance as a consequence of the process plan.

Figure 3: A balance beam balancing product performance and manufacturing

performance.

High cost!

High cost!Competitive

cost?

c)

a)

b)


If the decided manufacturing process cannot balance the product

design requirements, as shown in Figure 3 a, there will be a situation with

more out-of-specification parts. If the effect of this poor quality is

managed within the production system, for example by putting extra

efforts in measuring, sorting procedures, re-work and scrapping, the

production cost is assumed to be high (Fischer, 2011). If the poor quality

is not given enough attention and the parts reach a down-stream

customer, like the assembly line, the costs tend to be very high and even

worse, if the final customer recognises parts out-of-specification, there is

a risk of extremely high expenses.

The inverse situation (Figure 3 b) occurs when the manufacturing

process is specified to contain resources where the capabilities exceed the

requirements. The production cost is then assumed to be higher than

necessary because of unutilised performance of the manufacturing

resources. In addition, there is a risk of increased processing lead times

when using manufacturing equipment with unnecessarily high quality

capabilities (Mayer & Nusswald, 2001). This is a contributory cause for

high overall production cost.

2.5 The beam balance versus process capability

To elaborate the rather intuitive approach with the balance beam, I

have created a simplified causal graph model in Vensim2, shown in

Figure 4. This model includes the necessary underlying factors and the

relations to understand the balance beam and show why process

capability can be regarded as the other side of the same coin.

2 Vensim is software for “system dynamics” modelling. System dynamics is a methodology for

modelling complex, dynamic casual systems (Sterman, 2001). Vensim is also used later on to

simulate manufacturing processes (Bagge, et al., 2014).


Figure 4: Relations between product performance and manufacturing process

performance.

According to the right part of Figure 4, earnings when selling a

product are defined as the difference between the manufacturing cost and

the sales price. Sales price is highly dependent on the product

performance aimed to satisfy the customer3. Manufacturing cost is a

result of both manufacturing resource cost and, if any, an internal cost of

poor quality (CoPQ).

Manufacturing resource cost depends on the manufacturing process

performance. This relation is most frequently illustrated as a chart

showing possible tolerance width versus the manufacturing cost (left part

of Figure 5) (Manoharan, et al., 2012; Hamou, et al., 2006; Wu, et al.,

1998). An increasing tolerance width results in lower manufacturing cost

thanks to a decreased need for manufacturing process performance. This

can correspondingly be expressed as shown in Figure 5 b, where

increased manufacturing process performance drives higher

manufacturing cost.

3 The used curve in Figure 4 is just an attempt to illustrate a conceivable relation between sales price

and product performance.

Manufacturing

resource cost

Capability

Tolerance

width

Manufacturingcost

Sales price

Earnings

-

+

Manufacturing

process performance

Product

performance

Manufacturingprocessvariation

Lookup

CoPQ

+

Cost of

poor quality

+

Lookupmanuf cost

Balance

Lookupprice

1/x

1/x

-

External

Internal

Co

PQ

Process capability

Ma

nu

f. r

eso

urc

e c

ost

Manuf. process performance

=

Sale

s p

rice

Product performance


Figure 5: Manufacturing cost versus tolerance width (left) and versus process

performance (right).

The cost of poor quality in Figure 4 (Anderberg, 2012) is a result of a

low-capability manufacturing process that (too often) produces parts out

of tolerance. If the CoPQ appears internally, before delivery to the final

customer, it is here regarded as an undesirable contributor to the

manufacturing cost. If the customer is subjected to poor quality, the

CoPQ (external) will have a negative impact on earnings, no matter

whether it is put on the manufacturing account or elsewhere.

Capability originates from the relation between the tolerance width

and the manufacturing process variation and drives CoPQ if it is too low.

Tolerance width and manufacturing process variation are in turn the

inverses of product performance and manufacturing process performance

respectively. The quotient is a measure of the balance between these two

performances. This implies that the beam balance approach can be

supported by the use of PCI as an indicator for what is a well-balanced

manufacturing process design.

In the short term, imbalances between the product design and the

production facilities can be accepted. If the manufacturing process

capability is low, there may be a temporary solution to filter off bad

quality parts by additional measuring and checking (Anderberg, 2012). If

there are high performing resources available in the workshop, they may

be used in favour of investments in new, less sophisticated equipment.

As the production efficiency, final cost and product quality are affected

by the process plan (Chang, 2005), the desirable balance between product

performance and manufacturing process performance (Figure 5 c) is

definitely considered to be dependent on the work of the process planner.

Man

ufa

ctu

rin

g co

st

Tolerance width

Man

ufa

ctu

rin

g co

st

Manufacturing process

performance


2.6 Transmission part manufacturing

As already explained, the work of a process planner cannot be

unambiguously defined because it will contain different activities,

strategies and methods depending on situation. Typically, the type of

part, production rate, annual volumes, company strategies, etc. gives

different possibilities and constraints for process planning. The following

will give an insight into the area of industrial transmission part

manufacturing from which process planning experiences and the research

presented in this thesis are derived.

2.6.1 The transmission parts

The studied company both develops and manufactures transmission

parts in-house. Precision manufacturing is performed in workshop

facilities aimed at series production of gear wheels, pinions, gear box

shafts, etc. for heavy trucks, busses and coaches. Annual production

volumes for each category can be counted in terms of ten thousand to a

few hundred thousand parts. Each part is usually in production for at

least a couple of years. Figure 6 shows cylindrical gears for a gearbox and

bevel gears for rear axles.

Figure 6: Examples of transmission parts:

Bevel gear pinion (a), bevel gear ring gear (b),

counter shaft (c) and gear wheel (d).

2.6.2 The workshop

The manufacturing process for these kinds of parts can be divided into

three sub-processes; 1) soft machining, 2) heat treatment and 3) hard

machining, as shown in Figure 7.

a)

c)

b)

d)


Figure 7: A typical manufacturing process chain for gears.

These three sub-processes contains various multi-step machining or

heat treatment operations organised on different kinds of manufacturing

lines. A common line for soft machining contains one or two multi-

spindle lathes, machine tools for gear and spline cutting and some

equipment for chamfering/deburring. Heat treatment is normally case

hardening followed by tempering. Hard machining lines usually include

machine tools for hard turning, grinding, honing and gear grinding.

Depending on transmission part category, a complete manufacturing

process includes 6 to 12 set-ups, measuring excluded, for the part to

undergo before it is finished.

Each line is designed and equipped to produce one kind of

transmission part such as gear box gears, gear box main shafts, gear box

counter shafts, bevel gear pinions, etc. Depending on part design, the

fixtures, cutting tools and NC programs are changed to facilitate

manufacturing of variants within the desired operational range of the

line.

2.6.3 The process planning

As a consequence of the product design, most transmission parts are

able to be grouped or categorised, and the process plans are more or less

systemized in accordance with these part categories. For example, bevel

gear sets for the rear axle (one pinion and one ring gear) may have

different gear ratios depending on customer needs, but the gear housing

axle casing is the same. This is realized by using standardised interfaces

between gears and housing and then defining a range of gear sets that will

Raw

material

/blanksSoft machining Heat treatment Hard machining

Inspection and measuring

Carburiz-

ing

Quench-

ing

Temper-

ingTurning Drilling

Gear

cutting

Turning/

grindingHoning

Gear

grinding


fit. These standardised interfaces are utilised also in the manufacturing

process. All the pinions are manufactured in the same way, on

standardised machining lines with the same kind of machine tools. Most

of the cutting tools and fixtures are also the same, but with some specific

NC programming and gear cutting tools suited to the individual, gear

ratio dependent dimensions.

This structure is emphasised by the company strategy, advocating

both systematic part design and manufacturing process design, which in

turn characterises the process planning work.

The process planning methods can be regarded as mostly retrieval

because of this strategy. This implies that the process planning has more

of a continuous improvement and long-term perspective than handling

completely new product designs in a workshop with frequent

changeovers. Much effort can be put into getting proper designs for both

the manufacturing process and the part.

This industrial production environment is the cause and trigger of

many thoughts and ideas of methods for systematic planning of precision

manufacturing processes.

15

3 Problems to solve and research objectives

Two main process planning problems are discussed in this thesis. The

first has its background in my own industrial experiences and relates to

the need for different kinds of process planning methods. The other is

identified as a result of studies of related academic research about process

planning. The research objectives are a consequence of these two problem

definitions as illustrated in Figure 8.

Figure 8: The research objectives are derived from both an industrial and an

academic problem identification.


Process planning is a challenge because of the diversity of activities

required to design and evaluate a manufacturing process. Human

knowledge has to be involved, yet it is not possible to efficiently transform

this into CAPP systems because of the multi-perspective problem nature.

Because many people are usually involved in product and manufacturing

process development much process planning information has to be both

easily accessible and comprehensible for others than the process planner.

Tacit process planning knowledge and reasons for decisions have to be

explicitly described. The first issue when developing methods for process

planning is to facilitate systematic representation of qualitative

information. The other issue is to integrate methods for quantitative

process planning information.

Product design specifications are with only few exceptions based on

quantitative measures. Whatever the kind of acceptance criterion for

good quality is and what is regarded as an acceptable production cost,

there is a need to estimate the outcome of a proposed manufacturing





16 | PROBLEMS TO SOLVE AND RESEARCH OBJECTIVES

process. The most decisive question before finalizing a process plan is

whether the outcome of the manufacturing process will be as expected.

Will the parts comply with the specification? How often will an out-of-

tolerance part appear? The process planner must quantify and ascertain

the “truth” about the production system capabilities to be able to answer

these questions and define a process plan that ensures well-running

production.

Even if the produced parts fulfil the specifications, the manufacturing

process is not automatically well-utilised in terms of production system

capabilities. The mission for the process planner is to design the process

in a way that gives a good yield in terms of required quality but also low

cost thanks to a smooth running production with well utilised workshop

resources that are suited to the purpose.

In multi-step manufacturing processes, which are used for gear

transmission parts, there are many concurrent parameters that must be

considered and the process planner has many choices to make while

developing the process plan. The process plan must be more or less

detailed to avoid deviations due to variations in machine tool set-up,

cutting tool change, process control strategy, etc. There will often be a

broad range of possible solutions for bringing different process steps

together and define for example a control strategy for a complete

manufacturing process.

There is a need to analyse the effect of potential process plan

solutions, parameter settings, IPW tolerances, control strategies, etc. and

there has to be one or more methods available where these aspects can be

defined and tested. Predicting manufacturing process outcome and

making comparisons to the design part specification have to be integrated

parts of a framework of methods to support process planning.

There is a need for a framework of process planning methods

integrating both descriptive/qualitative and analytic/quantitative

parameters and able to reveal whether the proposed process plan is good

enough.


An introduction to the nature of process planning and how it is

defined in literature has been given in chapter 1. The following gives

further details of previous research efforts on topics related to the

identified problem.

PROBLEMS TO SOLVE AND RESEARCH OBJECTIVES | 17

The greater part of modern process planning research has been

devoted to CAPP. CAPP has challenged the manufacturing research

community for the last four decades and a huge volume of literature has

been published (Xu, et al., 2011). Though there are extensive efforts put

into CAPP research, and many different CAPP systems have been

developed, there is still a long way to go before process planners can

commonly use the technique. This was the overall conclusion drawn 20-

25 years ago about the state of CAPP research and implementation

(Alting & Zhang, 1989; ElMaraghy, 1993) and the situation is hardly

changed since (Xu, et al., 2011; Anderberg, 2012).

The problem with CAPP is characterised by many interdependent

parameters and variables (Denkena, et al., 2007), not only technical but

economical, organisational and environmental. However, this problem is

not peculiar to CAPP but characterises the complex and dynamic nature

of process planning in general (Ham, 1988), whether it is performed by

computers or humans.

As the process planner spends a great deal of time on creating and

administrating operation sheets, drawings, text documents, etc.

(Lundgren, et al., 2008; Halevi & Weill, 1995) there is an incentive to

rationalise and automatise such work. This is one of the topics that CAPP

research has been focusing on and what can be associated with research

areas such as product lifecycle management (PLM), computer-aided

design (CAD) and computer-aided manufacturing (CAM). As indicated in

the PPP (Figure 2) these kinds of activities are done when a process plan

is established and most of the process planning decision-making is

already done; for example, there are available definitions of operation

sequence, suitable machine-tools, tolerances and control strategies.

This thesis has its focus on the activities leading to establishing the

process plan according to the PPP, i.e. process planning decision-making,

roughly described as manufacturing process design. Here, the CAPP

research has been devoted to different approaches to reflect the heuristic

character of human-based process planning and develop computer

systems showing some traits of human intelligence. Artificial intelligence

(AI) has the goal of modelling human intelligence on computers, and has

been one of the main approaches for automated process planning.

Rzevski (1995) has listed five paradigms of AI in engineering:

knowledge-based systems, neural networks, genetic algorithms, fuzzy

logic and intelligent agents. Most of the AI-related CAPP research can be


classified according to these paradigms with examples given in Table 2.

Some of them are hybrids covering more than one paradigm.

Table 2: Examples of research in different paradigms of artificial intelligence (AI).

Paradigm of AI

Examples of research

Knowledge-based systems

(Kusiak, 1991; Xu, et al., 2009; Zhang, et al., 2012), including definition, storage and retrieval of relevant process planning information, often incorporated in a PLM system (Denkena, et al., 2007)

Neural networks

(Teti, et al., 1999) (Hua & Xiaoliang, 2010) (Amaitik & Kiliç, 2007)

Genetic algorithms

(Qiao, et al., 2000) - operation sequencing (Cai, et al., 2009) integrates process planning and scheduling (Li, et al., 2002) (Azab & ElMaraghy, 2007) Reconfigurable process planning (Nejad, et al., 2012) Model based tolerance analysis

Fuzzy logic (Amaitik & Kiliç, 2007) (Chen, et al., 1995) (Li & Gao, 2010) Integrates process planning and scheduling

Intelligent agents

(Li, et al., 2011) (Bose, 1999) Operation sequencing

Much of the AI–based CAPP research has an ambition to develop

systems that automatically optimise the process plan, whether it is about

operation sequencing, scheduling, tool selection, tolerancing or some

other task. Many of these systems are shown to be suitable and well

working for a highly delimited environment with well-defined

constraints, but are not up to the wide-ranging characteristics of a

complete process planning scope. Existing CAPP solutions do not have

the possibility to include for example business and strategic

considerations, which often have an impact on the choice of technology

and machining resources (Denkena, et al., 2007).

Finding research with the emphasis on comprehensive and heuristic

process planning methods and understanding is difficult. Although,

Timm et al. (2004 a, 2004 b) are good examples of where a heuristic and

graphical approach is used for developing a mathematical model to

support and link design dimensioning and process planning. Some

general design rules can then be derived and explained, based on the

model and the results.

PROBLEMS TO SOLVE AND RESEARCH OBJECTIVES | 19

Even more focus on the working processes, without the ambition of

AI, self-learning abilities or automatic optimisation, is a qualitative and

knowledge based approach to manufacturing process planning presented

as a part of “Produktionslotsen” (DMMS, 2009) (in English; the

production pilot) (Chen, et al., 2011). Produktionslotsen contains

systemised, model-based guides to support not only process planning but

other production-related activities such as factory planning, production

investments and continuous improvements.

A platform for model driven process planning for machining is

presented by Hedlind (2013). With this platform, not only the human

perspectives of visualisation and interaction of models can be satisfied

but also the technological aspects of standardised information

representation and exchange in a product realisation environment.

Probably, it is this kind of comprehensible approach, with the

potential to gather process planning intelligence, knowledge and

information to support efficient decision-making, that has to be

developed to bring CAPP one step towards industrial implementation. In

the CAPP review (25 years ago) Ham (1988) pointed out the importance

of detailed understanding of the task to be automated:

“In that sense, before pursuing any computer-based effort toward

process planning, it is necessary for us to move one step back and ask

ourselves: do we really understand the process planning task well enough

for computer automation? In other words, the fundamental questions

such as what is process planning, how is it being done presently in actual

practice, are there more logical ways to doing it must be answered before

any programming efforts should take place. Given even the current state

of computer technology, including artificial intelligence, it is still not

possible to develop computer programs to intelligently carry out tasks

unless we, as intelligent human beings, fully understand those tasks.”

In spite of the fact that computer technology has experienced huge

developments since 1988, lack of process planning understanding and

systematisation still seems to be a reason that restricts the relevance of

CAPP research and the successful industrial implementation.



Two research objectives are defined as a consequence of the identified

problems in sections 3.1 and 3.2.

Research objective 1

The first objective has an all-embracing aim of finding methods that

cover essential activities for process plan synthesis and analysis,

including the possibility to predict the outcome of a proposed process

plan. These essential activities are all found in the PPP flow chart in

section 2.1. Much attention is paid to understanding and analysis.

Research objective 2

The second objective is a result of the request for systematisation and

a deeper understanding of process planning, addressed by the academic

production research community. The aim here is to develop the PPP flow

chart on the basis of experiences and findings from the methodology

studies addressed in objective 1.

3.4 Delimitations

The last activity in the PPP flow chart “Establish process plan” is not

covered by the performed research; i.e. creating documentation required

for production, as operation sheets, control plans and NC programs.

Focus is on the underlying process planning efforts needed for its

creation.

Even though the manufacturing cost is an important factor it is not

included in this thesis other than in general terms as described in

section 2.

“Process performance” is just a matter of geometric dimensions, it

does not include any aspect concerning productivity, production rate or

volume capacity, for example.

Time related aspects such as machining cycle time or lead time

through a complete manufacturing process are not considered.

This thesis does not go into automatic optimisation procedures to find

for example appropriate quality or balanced tolerances.

Production scheduling and planning is not treated, nor process

planning for job-shops.

21

4 Research approach

The origin of the work presented in this thesis is years of production

engineering practice for the manufacturing of transmission parts such as

gears, shafts and synchromesh. Experiences and ideas are mainly derived

from my own process planning work, performed in an industrial

environment where the tasks have been both managing and improving

mature processes and products in addition to development of new ones.

The research has been characterised by adopting a methodology based

approach to meet the objectives stated in section 3.3. The approach is

comprehensive and aims to gather essential methods that should cover

the process planning domain as shown in Figure 2. The level of details is

not restricted and can include all four levels defined by ElMaraghy and

Nassehi (2013) in Table 1.

Strategies for defining tolerances and evaluation of manufacturing

process chains are examined with most of the focus on the detailed and

micro-planning levels. The purpose of examining these strategies is to

facilitate a well-substantiated answer to the final revealing question:

-Is the process plan good enough?

The work is also related to the request for more process planning

knowledge addressed by Ham (1988) in chapter 3.2. The approach to

meet this need is to refine the PPP by using results derived from the

methodology studies.

The research is not aimed at completing any existing CAPP approach

but to independently contribute to better process planning

understanding, without limitations in available computer-based process

planning aids.

4.1 R&D of systematic process planning methods

The presented research extends the qualitative approach for

systematic process planning (Bagge, 2009) to include quantitative and

analytical methods for design and evaluation of a process plan. A

conceptual illustration of the research and development (R&D) process

for this work is shown in Figure 9.

22 | RESEARCH APPROACH

Figure 9: Research and development of systematic process planning methods.

The licentiate thesis “An approach for systematic process planning of

gear transmission parts” (Bagge, 2009) covers descriptive and qualitative

methods for process planning, e.g. how to deal with product design

requirements, part and manufacturing datums, manufacturing operations

and manufacturing sequence, all with an explanatory approach. The left

part of Figure 9 represents this schematic process planning and the tacit,

but crucial, knowledge about process behaviour.

However, making analyses and predictions of the process plan

requires quantification both of the requirements, typically manufacturing

tolerances, and the behaviour of the included manufacturing processes.

The purpose of the new research is to reach the complete process

planning state , “To be”, on the right of the arrow in Figure 9, where not

only descriptive and qualitative methods are defined but also analytic and

quantitative methods. This part of the work corresponds to research

objective 1.

Research objective 2 is realised by using results derived from the work

for objective 1 and has not been published separately. Research objectives

1 & 2 and their relation to the performed research are showed in

Figure 10.

Schematic

process planning

Descriptive, qualitative

Capture of

process behaviour

Perceptual, qualitative

Schematic

process planning


Capture of

process behaviour

Quantitative

(Perceptual, qualitative)

Complete

process planning


Analytic, quantitative

Revealing

New research

As is To be

RESEARCH APPROACH | 23

Figure 10: Research objectives 1 & 2 related to the performed research.

5. Performed research and synthesis of results

4. Research approach

Refined PPP

Schematic

process

plan


Tolerancing

Process

control

strategy


Predicted

outcome

of process

Tolerance

analysis

Process

plan

concept

Initial

process

planning

YES

NO

Good

enough?D

Simulations

Design of

Experiments

Multivariate

data

analysis

Work instructions

Tool layout

NC-programs

Process

plan!

Establish

process

plan



Measuring programs

Assignment

directive

Control documents

F G

...

Part design

Workshop

Collaboration parties:

Manufacturing technology

BE

I

H

H

H

K

J

I

DC

A

In-process

tolerances

In-process

dimensions

Process behaviour

In-process tolerances

Product characteristics

Process settings

G

F

Part design

K

Framework of process

planning methodsLicentiate thesis

Paper A

Paper B

Paper C and D

Objective 1

Objective 2


3.3 Research objectives1. Find suitable methods for process planningi

2. Increase knowledge about process planning



PPP

24 | RESEARCH APPROACH

4.2 Positioning of appended publications

The appended publications in this thesis and their coverage are

mapped onto the PPP flow chart as shown in Figure 11. Experiences from

the FFI ReVer project have not yet been published but are partially

described in this thesis as an example of multivariate data analysis.

Figure 11: Coverage of appended publications upon the PPP flow chart.

Paper B

(Bagge

Lindberg

2012)

Paper A

(Werke

et al.

2014)

FFI

ReVer

project

Paper D

(Bagge et al. 2014)

Licentiate thesis

(Bagge 2009)Schematic

process

plan


In-process

work piece


Process

control

strategy


ResultTolerance

analysis

Process

plan

concept

Initial

process

planning

YES

NO

Good

enough?

Process


Process

behaviour

Design of

Experiments

Process

behaviour

Multivariate

data

analysis

Known

process

behaviour

Work instructions

Tool layout

NC-programs

Process

plan!

Establish

process

plan



Measuring programs

Control documents

...

Assignment

directive


Paper C (Bagge et al. 2013)

25

5 Performed research and synthesis of results

The five publications shown in Figure 11 are turned to account in this

thesis, and all discuss supporting methods for process planning. These

methods have been gathered into a framework for process plan

development shown in Figure 12 as a result of the research undertaken.

This framework is introduced here to provide an overview and better

understanding while reading the following summaries of the publications.

Figure 12: Framework of methods for process plan development.

The framework contains three vital process planning subjects

indicated in Figure 12:

1. Schematic process planning

2. Capture of process behaviour

3. Tolerance synthesis and analysis

Finished part

CAB ab

Turning

Retractable jawsFace

driver

a’b’A’B’End-machining and centre drilling

Parting line and

residual flash

a’b’

1 2

3

OE

Representation of IPWdimensions

Illustration of part geometry

Productdimensions

& tolerances

PSIPW

dimensions& tolerances

Mappingmatrix

FDProcess chain

dim. & tol.

OEIPW

dimensions

PSview of

IPW variation

OEview of

IPWvariation

Mappingmatrix

Representation ofproduct dimensions

PS


Comparison

26 | PERFORMED RESEARCH AND SYNTHESIS OF RESULTS

Each subject has one or more supporting methods to realise the

subject intentions, all outlined in this thesis.

Subject 1: Schematic process planning

The methodology for systematic process planning covers

interpretation and transfer of design requirements into the schematic

process plan. The schematic process plan facilitates explanatory

representation of machining or heat treatment operation elements,

process sequence and important decisions such as the choice of datum,

clamping surfaces and tools. The representation of measuring operations

and measuring equipment is done by the same principles. It is desirable

to highlight key features derived from both design specification and

manufacturing process development.

Subject 2: Capture of process behaviour

Subject 2 contains the supporting methods; simulation, design of

experiments (DOE) and multivariate analysis. The aim is to capture

knowledge and information about the behaviour and abilities of potential

manufacturing processes to be included in the process plan design.

Subject 3: Tolerance synthesis and analysis

The DDC2 technique is used to support the development of a process

plan regarding synthesis and analysis of tolerances. Different process

plan solutions, tolerancing strategies and control strategies can be tested

and the outcome of a contemplated or existing manufacturing process

predicted.

In the following sections, summaries of the appended publications are

presented after a short introduction to each subject.

5.1 Schematic process planning

The schematic process planning has been characterised as a

descriptive and qualitative part of the process planning domain. This has

mainly been dealt with in the licentiate thesis “An approach for

systematic process planning of gear transmission parts” (Bagge, 2009).

PERFORMED RESEARCH AND SYNTHESIS OF RESULTS | 27

5.1.1 An approach for systematic process planning of gear transmission

parts

The licentiate thesis (Bagge, 2009) introduces the field of process

planning of transmission parts, based on experiential research. What role

does the process planner play in different situations, what is the area of

responsibility, what motivates the use of systematic and transparent

process planning methods? Figure 13 characterises the interaction

between the process planner and collaboration parties while developing a

process plan.

Figure 13: The role of the process planner.

There is always an inflow of information from part design to process

planning, and often a possibility to feed back to part design. This depends

on the situation and where the designed part is in the product life cycle.

In product development stages it is common to have collaboration

between part design and process planning and here are considerable

possibilities to make design adjustments. When the product is mature,

possibly becoming a spare part, and a sub-supplier is assigned to start

production, the threshold for changing the design is conversely high. In

addition, collaboration with external providers of manufacturing

technologies very much depends on the situation and on company

strategies.

Often process planning is based on experiences and knowledge of the

process planner, performed by intuition, and without any explicit

Process Plan:

Methods

Operation sequence

Machining data

Tolerances

Cutting tools

Clamping tools

Measuring strategy

Part design

Workshop

facilities

Manufacturing

technology


procedures for working. The only documentation is the finally defined

process plan and the traceability of made decisions is poor.

The licentiate thesis proposes a method that facilitates visualisation of

the intentions of the process planner, the prerequisites and the

circumstances for made decisions. This makes the determining factors for

the resulting process plan transparent and comprehensible.

One important aspect of process planning is tolerancing, which must

be considered to complete the definition of how a part shall be

manufactured. Detailed tolerancing is not included in the licentiate thesis

but the principles of IPW tolerance chains (tolerance stack-ups) are

pointed out. Identification of relations between machining datums and

created features is described by using a datum hierarchy (rooted-tree)

diagram. Bagge ends up with the following conclusions about the

proposed method for systematic process planning (Bagge, 2009):

It describes a systematic approach to interpret new as well as mature

products.

It prescribes systematically how essential design characteristics should

be transferred to the process plan.

It points out how attention should be paid to datums

It guides the process planner through the evaluation of operations

It proposes a way to document the process planning work, aimed at

both immediate and future needs.

Further performed research and publications are along the same line

as the proposals expressed in the licentiate thesis; tolerancing,

quantifying performance of needed manufacturing processes and

capability testing of the developed process plan.

5.2 Capture of process behaviour

One precondition for creating a relevant process plan is to have

knowledge about the abilities and behaviour of the processes to be used.

This is essential when IPW tolerances and control strategy shall be

defined.

Depending on the situation and the exactitude of the process planning

work, different degrees of understanding of process behaviours are

required. It is possible, but not always advisable, to compensate for

deficient information about process abilities by defining (too) wide IPW


tolerances. This will result in low utilisation of the process performance.

If the process behaviour does not correspond to what is assumed, and the

IPW tolerances are too tight, there is an obvious risk of difficulties to

control the process and, as a result, getting parts out of specification.

Knowledge of manufacturing process behaviour can be captured in

many ways, in different situations and for different purposes.

This thesis identifies and deals with three different methods according

to Figure 14 for investigating the behaviour of complex processes. One or

more of these methods can be used to shed light on process behaviour

and bring valuable input data to the development of the process plan.

Figure 14: Methods to capture process behaviour.

When developing a new process plan for a new part, with new and

unrealised manufacturing processes, there will at the start be little or no

available knowledge about process capabilities. This implies the use of

methods for simulation to get one step closer to what is required.

Section 5.2.1 refers to Werke, et al. (2014) who proposes a methodology

for process modelling of manufacturing sequences.

If there is a need for new, or more detailed, information about

especially complex processes, DOE has been proven to support the

process planner (Bagge & Lindberg, 2012). This is further referred to in

section 5.2.2.

For established processes in workshops specialised for production of

high volumes of the same kind of parts, there is often a lot of production

data available. Work package A of the Realistic Verification (ReVer)

project has the intention to use available historical process and material

Process


Process

behaviour

Design of

Experiments

Process

behaviour

Multivariate

data

analysis

Known

process

behaviour


data to predict geometrical distortion of press quenched gears. A tool

based on data analysis should then support the process planner. This kind

of multivariate data analysis approach is discussed in section 5.2.3.

5.2.1 Paper A: Process modelling using upstream analysis of

manufacturing sequences

Simulations enable the examination of processes not only in the initial

development stages, but also when they are well established. One reason

for making simulations of established processes is to facilitate efficient

process start-up and control. For example, when material properties have

a significant impact on a machining or heat treatment operation, proper

process settings can be foretold by simulations.

Werke, et al. (2014) takes a step closer to efficient modelling and

simulation of manufacturing processes by proposing an algorithm for

upstream process modelling.

The methodology defines a set of rules for system modelling of

manufacturing sequences to be used in process planning. It facilitates the

selection of critical process parameter and an evaluation of how

variations in these parameters influence the final component properties.

The virtual manufacturing chain acts as an important source for

extracting and quantifying the process behaviour that is needed when

developing and controlling a process.

In contrast to the more intuitive downstream approach, the upstream

approach is governed more by the purpose of the simulations; not to

model a complete manufacturing chain and then make fragmentary

simulations. This is illustrated in Figure 15 where a conceptual framework

of models for making simulations to evaluate one or more properties of

the final part is defined. In this example, it is obvious that only some of

the process steps in the physical chain are represented in the virtual

chain. The reason is that only process steps affecting the finally examined

properties are modelled.

The principles of the upstream process modelling are along the same

line as the method for upstream process planning (Bagge, 2009)

illustrated in Figure 16.


Figure 15: Transformation of a physical manufacturing sequence into a framework

of models and simulations.

Figure 16: Process planning methodology compared to manufacturing sequence.

Process

step 4

Process

step 3

Process

step 2

Process

step 1

(First)

Process

step 5

Process

step 6

Sim.

4,1

Process

step 7

(Final)

Physical chain:

Framework of simulations:

Model data

Current

process

Process

param.

Sim.

4,2

Process

param.

Sim.

2,1

Model data

Current

process

Process

param.

Sim.

2,2

Model data

Current

process

Process

param.

Sim.

5,1

Process

param.

Sim.

5,2

Process

param.

Sim.

6,1

Process

param.

Sim.

7,1

Process

param.

Accumulated

results

Final

workpiece

Process

param.

Process

param.

Process

param.

Process

param.

Process

param.

Process

param.

Process

param.

First operation

Final operation

Manufacturing Process Chain

Process Planning


The process planner starts with the specification of the finished part

and defines the final manufacturing operation first. The work then

proceeds by defining required upstream process steps to fulfil the final

part specification.

Two cases in the paper exemplify the upstream process modelling

methodology; one for a bevel gear pinion and one for a forged steering

arm. The purpose of the virtual chain for the pinion shown in Figure 17 is

to simulate and reduce the accumulated displacements after case

hardening and in the end eliminate a straightening operation.

Figure 17: Framework of simulation activities for the pinion case study.

In conclusion, the paper proposes an algorithm to establish simplified

metamodels of manufacturing sequences using breadth first search. The

algorithm is based on stepwise upstream selection of process steps and

facilitates extraction of a virtual simulation sequence from a physical

manufacturing sequence.

The virtual sequences can be used for optimisation of process

parameters and evaluation of the effects of replacing, removing or adding

process steps to a manufacturing sequence.


Additional comments on the results in paper A

Even if each simulation case has its own focus and well-defined

demand for output data, there is a potential to derive more useful

information from simulations by using the same, or slightly modified,

models but ask for other characteristics. For example, the pinion case has

its focus on geometric displacements, but the simulation models used also

contain information about steel properties like carbon content, and

residual stress distributions (Werke, 2009). This information may be of

interest for the product designer as it has an impact on product

characteristics such as the strength of the pinion.

5.2.2 Paper B: Analysis of process parameters during press quenching

of bevel gear parts

In transmission part manufacturing, heat treatment processes (for

example case hardening) are commonly used to increase the strength and

wear resistance of the produced parts (Lingamanaik & Chen, 2012;

Arimoto, et al., 2011). A well known drawback of the case hardening

processes is the more or less predictable geometric distortion (Holm, et

al., 2010). These distortions have a broad range of possible causes with

not fully known physical properties and mechanisms which make process

control difficult (Funatani, 2011; Canale & Totten, 2005).

In order to minimise distortion and control the quality, press

quenching is a commonly used method in industry. Bagge and Lindberg

(2012) has shown the potential to use DOE as a method for getting more

knowledge about press quenching of bevel gears. The results enable

definition of IPW tolerances satisfying a pre-defined acceptance level of

defect parts.

It would have been desirable to have a deep understanding of how

material characteristics and phase transformations influence the result,

but the aim of the paper was to find possible statistical relations between

process control factors and their impact on dimensional properties.

Figure 18 show the bevel gear crown wheel and press quenching set-up

used for the DOE investigation.


Figure 18: Investigated bevel gear crown wheel (top)

and set-up in a press quenching machine (bottom).

The investigated properties are the centre-hole diameter (Ø240) and

conical properties of the back face (dishing) according to Figure 19.

Figure 19: Crown wheel back-face dishing.

Despite the press quenching machine having a pressure setting for an

expanding mandrel, aimed to influence Ø240, the effect is not obvious by

studying historical production data. Data for Ø240 versus the

corresponding expander pressure setting shown in Figure 20 does not

correlate as expected.

65

Ø480

Ø240Back face

Fexpander

FinnerFouter

Quench oil flow

Cradle

+

-


Figure 20: Historical production data for diameter 240 mm, compared to the

corresponding expander pressure.

This is an example when the outcome of a manufacturing process is

not simply deduced and understood by observing existing data.

The DOE matrix included not only the expander pressure setting, but

also six other factors supposed to influence the investigated responses:

Ø240 and back face dishing.

The multivariate method used for estimating regression models for

both responses simultaneously was the partial least square method. The

coefficients used in the models are shown in Figure 21.

Figure 21: Left: Coefficient plot and error bars (95%) for the response Ø240

Right: Coefficient plot and error bars (95%) for the response dishing.

0

10

20

30

40

50

60

239,5

239,6

239,7

239,8

239,9

240,0

240,1

240,2

240,3

240,4

Pre

ssure

[bar]

Dia

mete

r [m

m]

Ø240 Tolerance limits Expander pressure

-0,03

-0,02

-0,01

0,00

0,01

0,02

0,03

0,04

Inn

er

Ou

ter

Exp

an

Cra

_(C

1)

Cra

_(C

2)

Mtr

l_(M

1)

Mtr

l_(M

2)

Le

ve

l

[mm

]

N=55 R2=0,837 RSD=0,0236

DF=48 Q2=0,790 Conf. lev.=0,95

-0,04

-0,03

-0,02

-0,01

0,00

0,01

0,02

0,03

0,04

Inn

er

Ou

ter

Cra

_(C

1)

Cra

_(C

2)

Mtr

l_(M

1)

Mtr

l_(M

2)

Le

vel

Inn

er*

Inn

er

Inn

er*

Ou

ter

[mm

]

N=55 R2=0,891 RSD=0,01809

DF=47 Q2=0,789 Conf. lev.=0,95


The regression models then facilitate estimations of the impact of

individual factors and interactions of factors on the responses. The

number of significant factors affecting each response (Ø240 and back face

dishing) indicates the complexity of the process.

The regression models have then been used to make predictions of the

press quenching process outcome. Making process outcome predictions is

a desirable feature from a process planning point of view.

The predictions were made using the Monte Carlo simulation

technique and facilitate the estimation of the number of parts out of

specification, quantified as defect parts per million opportunities

(DPMO).

Bagge and Lindberg (2012) defined four scenarios which were

examined regarding the estimated DPMO for both Ø240 and back face

dishing. The scenarios were defined as combinations of two possible

cradles (C1 and C2) and two different materials (M1 and M2). The

predicted distributions and DPMO for each scenario are shown in Figure

22 and Figure 23.

Figure 22: Simulated distributions and DPMO for Ø240.

0

5000

10000

239,95 240 240,05 240,1 240,15 240,2

Counts

Ø240 [mm]

C1 M1DPMO=0

C1 M2DPMO=0

C2 M1DPMO=0

C2 M2DPMO=0

L. Tol. U. Tol.


Figure 23: Simulated distributions and DPMO for dishing.

The DOE investigation and the consequent DPMO predictions are

useful for the process planner because they give important information

about the behaviour of the process and provide a good decision base. In

this case, the possibility to tighten the tolerance for Ø240, block certain

scenarios (C1 M1) or widen the tolerance for dishing can be useful when

designing a well-balanced and competitive manufacturing process.

To conclude Bagge & Lindberg (2012) :

Design of experiments is a proper method capable of examining the

complex process of press quenching.

Regression models can in combination with Monte Carlo simulations

be used to estimate the outcome of different press quenching

scenarios.

The simulation results make it possible to perform an IPW tolerance

analysis by estimating DPMO for each scenario.

Design of experiments is a method suitable to support process

planning regarding process knowledge and tolerancing.

0

10000

20000

-0,06 -0,05 -0,04 -0,03 -0,02 -0,01 0 0,01 0,02 0,03 0,04 0,05 0,06

Counts

Dishing [mm]

C1 M1 0,04: DPMO=832 0,05: DPMO=26

C1 M2 0,04: DPMO=38 0,05: DPMO=0

C2 M1 0,04: DPMO=6 0,05: DPMO=0

C2 M2 0,04: DPMO=6 0,05: DPMO=0

L. Tol.U. Tol.


5.2.3 Multivariate data analysis

In many cases data from for example machine tool capability tests,

cutting tool tests or measurements from running production are available

after years of production. This data should be a good input to the process

planner for improving an established process plan or bringing a new

variant of a part into production. Depending on the complexity of the

process to be evaluated, the diversity of parameters taken into account

and data quality, data has to be handled in a more or less sophisticated

way.

For example, the relation between cutting tool wear, resulting in a

gradually shifting dimension, and the produced number of parts may be

good enough to predict process behaviour. However, if the result depends

significantly on a diversity of factors such as the number of produced

parts, material batch characteristics, surface roughness from an earlier

operation, temperature in the workshop, etc., multivariate analysis is a

more appropriate method.

This approach, where production data is used as a base for finding the

behaviour of a complex process, is used within the FFI project Realistic

Verification (ReVer). The aim of the project is to arrange and analyse

available production data in a way that geometrical distortions of a bevel

gear crown wheel can be derived. The production data includes many

manufacturing process parameter settings, material properties and

information about the origin of the raw material. Unfortunately, it has

shown to be a challenge to make the required multivariate data analysis

and get reliable results.

The reason is the great number of parameters having a potential

impact on the geometrical distortion of the bevel gear crown wheel, and

finding out which of them, and how they influence distortion, has been a

topic for research for many years. Figure 24 shows a commonly

recognized diagram where a multiplicity of parameters is identified, all

with a potential to have an effect on the distortion.


Figure 24: Potential reasons for distortions. Based on Funatani, IMST Institute

IDE2011, Bremen.

A large amount of production data has been sampled and organised

for analysis in the ReVer project. The great number of potentially

significant parameters in combination with uncertainty about the validity

of some data makes an evaluation difficult. Despite these circumstances,

some significant correlations between factors and responses have been

identified within the project.

The experiences show that it is not so easy to derive relevant

parameters from the available production data without having a good

hypothesis as a start. As in DOE, the chance of getting good results when

analysing complex processes much depends on the initial knowledge and

thoroughly reviewed assumptions. The better the hypothesis, the better

the chance to succeed with the multivariate analysis.

As much of the process planning work is knowledge intensive,

multivariate analysis has a potential to be a good long-term contributor

that knowledge. However, it requires a systematic approach to store

production data with enough quality to be analysed. It also requires good

skills in multivariate data analysis methods, as instantiated by SIMCA4.

5.3 Tolerance synthesis and analysis

Tolerance chains, or stack-ups, are well known within the product

design domain but not usually recognised in production engineering,

though they are undoubtedly present in many manufacturing processes.

The appearance of tolerance chains and streams of variation (Ceglarek, et

4 SIMCA is software for multivariate analysis developed by Umetrics.

www.umetrics.com

Composition

Hardenability

Grain size

Rolling

Refining

Melt lot

SteelMethods

Lot

Residual stress

Shape

Deform rate

Equipment

Cold formingMachinability

Residual stress

Cut volume

Tool sharpness

Cut condition

Machine

Machining

Furnace

Temperature

Quenchant

Agitation

Pressure

Tank design

Quenching Press

Force

Tool design

Temperature

Strategy

Time

Weight

Core hardness

Surface hardness

Case depth

Shape

Mass balance

Part design

Die set

Machine

Fiber flow

Forge rate

Post cooling

Heating

Temperature

Forging

Timing

Cooling

Temperature

Duration

Furnace

Thermal cycle

Annealing

Timing

C-potenial

Temperature

Furnace

Tray & Jig

Atmosphere

Carburizing

Furnace

Cooling

Duration

Temperature

Pre heat treatment

Distortion


al., 2004) in machining processes are similar phenomena to those found

in assemblies.

Design of multi-step manufacturing processes can be compared to

product design of an assembly containing several parts. The assembly

must be suited to the intended application and satisfy the needs of the

customer. This requires that each constituent part of the assembly has an

appropriate design and that they all are assembled in the right way.

Putting the parts together often requires a specified sequence. An

important aspect of designing assemblies is tolerancing of the parts. The

part tolerances must be specified in a way that they, when combined,

assure the function of the assembly. This combination of part tolerances

forms one or several tolerance chains.

In contrast, designing a multi-step process, such as for machining,

does not aim to specify how to make an assembly, but how to combine

different manufacturing processes to produce one final part in a proper

way. This combination of manufacturing processes does often result in

tolerance chains that determine how well the design part specification can

be met.

One important difference between design part tolerances and process

plan tolerances must be emphasised. The product designer appends

requirements to each design part as e.g. dimensional tolerances, and

sometimes to the complete assembly. The design part tolerances are

mainly on functional requirements arising from the intended application

of the part. From the process planner’s point of view; the IPW tolerances

must guarantee satisfaction of these requirements, but also process

related needs in the different stages of manufacture. These process-

related functional needs are important to carry out for each

manufacturing step. For example, in multi-step manufacturing where the

workpiece is moved between fixtures in different machine tools, or just

re-positioned and clamped in the same fixture, one or more features act

as locating surfaces. These surfaces have the in-process function of

making accurate fixturing of the workpiece possible. Another example is

when a measuring operation requires special surface characteristics other

than those defined by the product designer.

The process planner has to ensure the quality of the final part, not by

copying design part tolerances, but by defining both prerequisites and

allowed outcome for each process step as IPW tolerances. These IPW

tolerances are sometimes, but not always, equal to the design part


tolerances and must, when combined, satisfy the design part

requirements.

This subject has been dealt with in two of the appended publications.

The main development of a method for tolerance chain design and

analysis was done by Bagge, et al. (2013) when first introducing the

dimension dependency chart (DDC). The DDC was then further

developed to become the DDC2 and used as a workbench for evaluation

of different tolerancing strategies (Bagge, et al., 2014).

5.3.1 Paper C: Tolerance chain design and analysis of in-process

workpiece

Bagge, et al. (2013) highlights the relations between the process plan5

and the product specification, but also between the process plan and the

basic manufacturing operations of which the process plan is composed.

The relations between these basic manufacturing operations, called

operation elements (OE), the process plan and the designed product

specification are shown in Figure 25.

Figure 25: Relations between operation elements, process plan and designed

product specification.

5 “Process plan” represents in this case only the process steps, their sequence, nominal dimensions and

tolerances. To be compared to the general description of process plan given in chapter 2 at page 5.

Process plan

Operation element

Operation element

Operation element

Operation element

Operation element

Operation element

behaviour

Dimensions

and tolerances

Designed product

specificationComparison


The following gives a more detailed explanation of what an OE is:

Each process step in the process plan defines a task to be performed, for

example turning of a surface, and how the result is to be evaluated. Even

if this process step is realised by just one turning operation, the method of

evaluation may entail that the shown result depends on more than one

operation. Typically, this situation appears when a distance between two

machined surfaces needs to be measured and evaluated. The distance is a

result of the position of two surfaces created in not only the last, but also

in a previous operation. The definition of the process step therefore

includes two single operations, which are the “operation elements“. The

process plan is developed by defining and combining suitable operation

elements capable of producing the final part.

A method for the development of a process plan in terms of

dimensions and tolerances is described and facilitated by the dimension

dependency chart (DDC). The DDC is a structure based on the tolerance

charting technique where the design part tolerances, the IPW tolerances

and the operation element variations are connected. A conceptual

description of the DDC is illustrated in Figure 26 where information flows

between both design specification and process plan (upper view), and

between OE and process plan (lower view) are shown.

Figure 26: A conceptual illustration of information flow in the dimension dependency

chart (DDC).

Upper

view

Process plan

Design specification

Process plan

Operation element behaviour

Lower

view


The DDC shown in Figure 27 has the capability to include and gather

both operation element behaviours and process step definitions and make

a comparison between the allowed outcome of the proposed

manufacturing process and the design specification. Details of the

procedure to establish a DDC are given in the appended paper C.

Figure 27: DDC – Dimension dependency chart.

1 2 3 4 5 Tol TE SE RE

X F P. step 1 1 0,5 0,3 0,2

X P. step 2 1 0,4 0 0,4

X F P. step 3 0,5 0,4 0 0,4

X F P. step 4 1 1 0,6 0,4

F X P. step 5 1,2 0,5 0,3 0,2

X F P. step 6 0,1 0,2 0,1 0,1

SE RE

X F Element 0,3 0,2 1 1 1 1

X Element 0,3 0,2 -1

X F Element 0,3 0,2 -1

X F Element 0,3 0,2 1

F X Element 0,3 0,2 1

X F Element 0,1 0,1 1

Comparison

Symbols:

Design dimension

Machined feature and extent of dimension

F Finally machined feature

X Machining location and datum surface

1 2 3 4 5 Dim Tol Dim Tol

50 2 50 2,3

20 2 20 0,1

20 3 20 2,8

20 4 20 1

Dim Tol

X F P. step 1 71 1

X P. step 2 49 1 1 1

X F P. step 3 30 0,5 -1

X F P. step 4 20 1 1

F X P. step 5 21 1,2 1 1

X F P. step 6 20 0,1 -1 1 -1

Comparison

+-

Lowerview

Upperview


The concluding remarks from Bagge, et al. (2013) are:

The paper introduces the DDC for tolerance chain design and analysis

of an IPW.

The methodology is a novel approach based on the tolerance chart

technique to define, include and connect operation element behaviour

to the process plan and evaluate the expected outcome of the process

chain.

The DDC facilitates quantification and integration of smart process

planning practice in the process plan and enables analytical

calculation and evaluation of the results.

5.3.2 Paper D: Process chain based workpiece variation simulation for

performance utilisation analysis

As mentioned, the DDC was first introduced by Bagge et al. (2013) as a

methodology for process planners to design and evaluate in-process

tolerance chains. Figure 28 shows the second edition of the dimension

dependency chart (DDC2) which is a result from elaborating the DDC

methodology to facilitate the use and analysis of more substantial and

detailed input data (Bagge, et al., 2014).

Figure 28: DDC2 – Dimension dependency chart 2.

OE



Productdimensions

& tolerances

PSIPW


Mappingmatrix

FDProcess chain

dim. & tol.

OEIPW

dimensions

PSview of

IPW variation

OEview of

IPWvariation

Mappingmatrix

FD = Final dimensionIPW = In-process workpiecePS = Process stepOE = Operation element


PS


Comparison


The IPW tolerances for each PS are set by the process planner to limit

the permissible IPW shape deviations. In addition to the IPW tolerances,

it must be possible to satisfy the specification of the final part with the

manufacturing process defined in the process plan. The process planner

must evaluate each PS and the complete process chain regarding

capabilities to fulfil all requirements.

As discussed in section 2.2, the calculation of PCI is a commonly used

method to indicate the abilities of a manufacturing process to produce

within tolerance. Despite the statistical condition of normally distributed

process outcome to estimate the standard deviation and calculate for

example Cp, judgements based on PCI appears to be applied even when

that condition is not met. Many manufacturing processes do show trends

caused by tool wear, temperature changes, etc. and are often dependent

with correlated outcomes implying non-normally distributed data.

The first objective in the paper was to evaluate PCI as an indicator in

analysing IPW tolerance chains, a task in planning of multi-step

manufacturing processes. The second objective was to examine how the

characterisation of causes of deviation in the process chain and its

behaviour, affects the performance utilisation of the defined process.

The research was performed by developing and using the DDC2 as a

workbench for analysis of the process plan and evaluating how various

strategies for tolerancing and use of process data result in different

utilisation of the manufacturing equipment performance.

As in paper C, the subject for the process plan was a shaft, here

referred to as the “Xshaft”, shown to the left in Figure 29. The Xshaft is a

fictitious part but it has distinctive features found in typical transmission

parts like the gear shaft to the right in Figure 29.

Figure 29: Xshaft (left) and gear shaft (right).

50± x

20±x20±x20 ± x

Xshaft1:1 02014


The Xshaft is proposed to be machined in three set-ups. The

contribution from each OE to the PSs defined in the process plan

continues to the final part dimensions as illustrated in Figure 30.

Figure 30: Manufacturing process for the Xshaft.

The focus for the research was to evaluate what can be achieved from

the available manufacturing facilities when used as defined in the process

plan. The manufacturing facilities have been assigned with behaviour

characteristics, like tool wear and random variations, to represent

industrial conditions. Each operation is defined as an OE and the ability

to create a feature on the Xshaft depends on the assigned behaviour of

that OE.

The aim of the investigations is to find out the tightest tolerances on

the FDs that the proposed manufacturing process chain can provide. The

tighter the tolerance, the higher the performance of the process, and

better the possibility for the part designer to increase the performance of

the product. The evaluation criterion was the potential process

performance utilisation (PUR) when different strategies for tolerancing

and use of process behaviour definitions are applied to the process plan.

Five simulation scenarios were defined. Four of the scenarios

represent different IPW tolerancing strategies in combination with

different characterisation of process behaviour information. The fifth

scenario is the reference where the “pure process behaviour” and its

propagation through the process chain determines the process outcome,

Operation elements

OE1

OE2

OE3

OE4

OE5

OE6

Process steps

PS1

PS2

PS3

PS4

PS5

PS6

Final dimensions

FD1

FD2

FD3

FD4Set-up 2

Set-up 3

Set-up 1


without any limitations such as IPW tolerances and simplified

representation of process behaviours.

The reference scenario was defined to have 100% process PUR. All

other scenarios showed to have lower or much lower PUR for one or more

of the evaluated FDs.

As this paper considers manufacturing processes showing not only

random but also systematic variations, the significance of these

systematic variations (expressed as SE) is of interest. What outcome will a

virtual workshop with more pronounced SE than RE show compared to a

workshop where SE and RE are similar?

To show the impact of different workshop characteristics two

examples were defined: virtual workshop 1 (VW1) and 2 (VW2).

VW1 has SEs and REs with the same proportions; SE/RE quotient is

approximately 1. VW2 has SEs double the REs and consequently the

quotient is 2.

The simulations were performed using Vensim DSS software (by

Ventana Systems, Inc.) together with the DDC2. In total, 100 000 Xshafts

were produced virtually for each scenario to avoid statistical uncertainties

related to random factors. Figure 31 shows input and output data

provided by Vensim for the simulation model based on the DDC2.

Figure 31: Vensim simulations applied on the DDC2.

Symbols:

Design dimension

Machined feature and extent of dimension

F Finally machined feature

X Machining location and datum surface

1 2 3 4 5 DimDP TolDP TolFD DimFD

1 Design dim. 50 50

2 Design dim. 20 20

3 Design dim. 20 20

4 Design dim. 20 20

SEPS REPS TEPS TolPS DimPS

X F P. step 1 71

X P. step 2 -49 -1 -1

X F P. step 3 -30 1

X F P. step 4 -20 -1

F X P. step 5 -21 -1 -1

X F P. step 6 20 -1 1 -1

SEOE REOE DimOE

Normal

direction

-1 -1 -1 1 T1 X F Element 71 1

1 T1 X Element 22 1

1 T1 X F Element 41 1

1 T2 X F Element 51 -1

1 T3 F X Element -21 -1

1 T4 X F Element 20 1

+-

T1

T3T1

T4

T2

T1

70,98

71,01

71,04

1 50Part instance no.

Dimension PS1

70,98

71,01

71,04


Dimension OE 1

49,98

50,01

50,04


Dimension FD1

-0,03

0

0,03

50

Part instance no.

RE OE 1

0

0,02

0,04

0,06


Tool wear T1-T4


Figure 32: Simulation results - Performance utilisation for FD1-4.

Figure 32 shows results from the simulations, in total 10 for each FD.

It is important to make clear that VW1 and VW2 represent two different

conditions to demonstrate how the workshop characteristics affect the

result in general; they do not compete with each other. The aim is to do a

sensitivity analysis regarding the impact on PUR related to the relation

between SE and RE for the involved processes.

A complete interpretation of the results is given in the appended

paper. From the results the conclusions are:

Using Cp in the IPW tolerancing strategy decreases the PUR, especially

for high Cp values and long process chains. In the presented case study

the use of TE instead of Cp=1,33 gives a higher PUR.

Separating SE and RE is useful if there are coupled, or semi-coupled,

OEs. SE for the PS will in these cases be eliminated or reduced.

Simplified definitions of process behaviour result in an

underestimation of the process performance. The longer the process

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

PS: Cp PS: TE OE: TE OE: SE&RE Reference

Pe

rfo

rma

nce

utilis

atio

n r

atio

FD1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


FD2

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Perf

orm

ance u

tilis

ation r

atio

Scenario

FD3

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Scenario

FD4

Performance utilisation ratio (PUR)

Virtual Workshop 1

Virtual Workshop 2

FD1

FD4FD3FD2


chains, the bigger the underestimation. Detailed definitions and

knowledge about process behaviour are vital for high PUR.

PUR is a potential element of a key performance index for process

planning.

Additional comments on the DDC2

General methods for tolerance charting often include abilities to make

stock removal calculations for cutting operations. Blank dimensions and

tolerances together with stock removal calculations have also been a

subject for integration into the DDC2. The attempts have shown to be

useful in practice, but at the time of writing, no research results have been

published.

5.4 Synthesis of results

The triangular framework for process plan development (Figure 12) is

synthesised as a result of the examined process planning methods

described in sections 5.1-5.3.

5.4.1 Information flows

Even though the process planning methods have been introduced and

observed as separate issues, each one requires more or less information

from the neighbouring subjects inside the triangle.

There is also an important interaction and information exchange with

the external collaboration parties “Part design”, “Workshop facilities” and

“Manufacturing Technology”, adopted from Figure 13.

If the available workshop facilities do not fulfil the needs, a

development or investment in new manufacturing technologies must be

considered. The specification of requirements on such technologies

mainly derives from the proposed conceptual or schematic process plans.

Information flows both inside and outside the triangle have been

identified and are indicated in Figure 33 as arrows A-K.


Figure 33: Triangular framework with information flows.

The information flows are provided by the capabilities of each subject

in the triangular framework and aims to satisfy both internal and external

needs during manufacturing process development.

Table 3 specifies the information flows corresponding to the arrows

A-K in Figure 33.

Part designManufacturing

technology

Workshop

facilities

Finished part

CAB ab

Turning

Retractable jawsFace

driver

a’b’A’B’End-machining and centre drilling

Parting line and

residual flash

a’b’

1 2

3

DC E

B

A

G

F J

K

H I

OE



Productdimensions

& tolerances

PSIPW


Mappingmatrix

FDProcess chain

dim. & tol.

OEIPW

dimensions

PSview of

IPW variation

OEview of

IPWvariation

Mappingmatrix


PS


Comparison


Table 3 Information flows according to arrows A-K in Figure 33.

Internal information exchange:

External information exchange:

A Process sequence Operation elements Key-features

F

Product specification: dimensions, tolerances and key-features

B Process behaviour IPW tolerances Product characteristics

G

Manufacturing prerequisites Part design suggestions Obtainable part tolerances Product characteristics Manufacturing knowledge (Manufacturing cost)

C

Process sequence Operation elements Measuring datums Machining datums IPW dimensions

H Manufacturing resources Process characteristics Production data

D IPW tolerances IPW dimensions Predicted outcome of process

I

Process plan Process settings

E Process behaviour IPW tolerances

J

Manufacturing technology characteristics

K

Request for manufacturing technology


5.4.2 Refining the PPP with information flows

The PPP was first assigned in chapter 1 to describe the process

planning domain as a process flow chart. To take the knowledge about

process planning practice one step further, the content of the PPP can

now be refined by adopting the identified information flows. These

information flows indicated as A-K and the collaboration parties

“Workshop facilities” and “Manufacturing technology” are brought into

the PPP flow chart as shown in Figure 34 at page 53.

However, the information flows cannot be unambiguously transferred

to the PPP because it has a different structure to the triangular

framework. The triangular framework represents methods connected to

each other in terms of information flow interfaces. The PPP is a

description of the process planning process where different activities,

supported by methods, both require and are providers of information.

This leads to some flows appearing in more than one place in the PPP

and sometimes does not include all the elements of information defined

in Table 3 at page 51. This is the case for information flow “I” which is

found in two positions, but the information content for each position is

clearly shown in the flow chart to be “Process settings” and “Process plan”

respectively.


Figure 34: Information flows applied to the PPP.

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55

6 Discussion and conclusions

6.1 Coverage of the research results

A number of methods to support process planning have been

presented in Chapter 5. The coverage of all these methods on the PPP is

summarised in Figure 35.

Figure 35: Final coverage of presented methods on the original PPP.

There are two areas within the PPP indicated by dotted lines;

multivariate data analysis and defining process control. These are

included in the work but have been the focus of less research attention.

Schematic

process

plan


In-process

work piece


Process

control

strategy


ResultTolerance

analysis

Process

plan

concept

Initial

process

planning

YES

NO

Good

enough?

Process


Process

behaviour

Design of

Experiments

Process

behaviour

Multivariate

data

analysis

Known

process

behaviour

Work instructions

Tool layout

NC-programs

Process

plan!

Establish

process

plan



Measuring programs

Control documents

...

Assignment

directive


56 | DISCUSSION AND CONCLUSIONS

However, both areas are seen as important because they contribute to

the design of competitive process plans.

Multivariate analysis has a potential to contribute because it facilitates

extraction of process behaviour from many different kinds of

unstructured production data. However, it is a challenge to derive useful

information if production circumstances have been changed and there is

no possibility to trace the changes.

A process control strategy is more or less mandatory and must be

defined in harmony with the rest of the process plan, especially tolerances

and measuring capabilities. The DDC2 methodology together with

simulation software, like Vensim, can be used for evaluation of different

control strategies. As shown in Paper B (Bagge, et al., 2014), using the

same cutting edge for two conjugate surfaces reduces the need for process

control without the risk of dimensional deviations. On the other hand,

when this is not possible, there is still a potential to increase the

utilisation of process performance by a well-defined process control

strategy. This can be shown by assigning for example imperative tool

change intervals and sequences for two or more cutting tools. It is also

possible to integrate the effect of measuring and control strategies like

statistical process control. The efforts put into process control can then be

compared to the PUR.

6.2 Follow-up of research objective 1

The main result of the methodology studies is the triangular

framework providing both qualitative and quantitative supporting

methods for process planning. Figure 36 revives the illustration from

Chapter 4 where the desirable characteristics of complete process

planning methods are indicated as; descriptive, qualitative, analytic,

quantitative and finally revealing.

DISCUSSION AND CONCLUSIONS | 57

Figure 36: Update of “Research and development of systematic process planning

methods“.

The attained characteristic of each subject in the triangle does

correspond to the conceptual representation of the research and

development process leading to methods for “complete process

planning”.

Descriptive and qualitative:

All methods in the framework have good potential to describe, explain

and visualise the mechanisms and decisions behind a process plan. Both

how single manufacturing process steps are defined and intended to work

and how they interact when combined into process chains. All examined

methods to capture process behaviour (simulation, DOE and multivariate

data analysis) require some kind of computation software whose user

interface considerably determines the descriptive qualities.

Analytic and quantitative:

The methods to capture process behaviour are all both quantitative

and analytic, likewise the DDC2 for tolerance synthesis and analysis.

Revealing:

As both single manufacturing process steps and process chains can be

analysed in terms of predicting the outcome, the methods have the

capability to reveal the requested qualities of a process plan.

As is To be

As is

Schematic

process planning


Capture of

process behaviour

Perceptual, qualitative

Schematic

process planning


Capture of

process behaviour

Quantitative

(Perceptual, qualitative)

Complete

process planning


Analytic, quantitative

Revealing

New research


6.3 Follow-up of research objective 2

Interactions realised by the identified information flows must be

emphasised as vital for all process planning work; either the work is done

by systematic methods, automated or manual, or by an experienced

process planner using intuitive, informal and undefined procedures.

Many of the information flows can by intuition be defined by someone

with process planning experience, but not surely with distinct reasons for

why they exist and for what they are used. The information flows related

to the framework in Figure 33 have their origins in verified demands and

opportunities of the proposed process planning methods, which in turn

support important process planning activities.

These information flows, based on needs and opportunities, are used

to refine and give more substance to the PPP, gaining better

understanding of the process planning domain.

6.4 The relation between the PPP and PPAP

Many manufacturing companies are subcontractors to original

equipment manufacturers (OEM) that demand conformity in quality

assurance methods and harmonized documentation of the results. An

example of such a duty is the production part approval process (PPAP)

(AIAG, 2006) often used to satisfy the international standard ISO/TS

16949. PPAP is also used by OEMs to assure quality and smooth

deliveries from organisations within the company. The aim with PPAP is

that the supplier must provide the evidence that the part will correspond

to the design specification and possible to produce at a quoted production

rate.

Many of the required investigations to provide that information are

included in the process planning work. The reason is simply that the

process planning has the same goal as requested by the PPAP. How PPAP

is related to the PPP is shown in Figure 37.


Figure 37: The relation between PPP and PPAP.

Today, PPAP requires test-runs as “significant production run” or “full

run test” to check the real outcome of the manufacturing process. By

improving process planning methods to ensure reliable predictions of the

process outcome, test-runs will probably be of less interest since they will

show similar results to the predictions. This situation is also desirable for

efficient process planning because the goodness of a proposed process

plan can be estimated before it is finally established, without putting

efforts into the creation of work instructions, control documents,

NC-programs, etc. that must be changed.

Another reason is that a test-run is performed during a limited period

of time, without possibilities to test for example variations in material

properties, workshop environment and idling machine-tools. Such

sources for process variations are common in a workshop and may be

important factors to analyse before start of production.

6.5 Process capability index

As described in section 2.2, PCI is a commonly used measure of the

capacity of a manufacturing process and is related to the balance between

product and process performance. PCIs, including directives of required

Process

plan

concept

YES

NO

Good

enough?

Known

process

behaviour

Process

plan!

Establish

process

plan

Predicted

process

outcome

Result

Significant

production

run

Process Planning Process (PPP)

Production Part Approval Process (PPAP) Approved

PPAP

Process flow diagram

Control plan

Initial process studies


levels, are more or less defined by both corporate standards and trade

association standards like PPAP 4th ed. and APQP published by AIAG6.

It is clear that for example Cm acts as a contributor of knowledge about

the behaviour of one single machine tool and Cp of a complete process in

relation to the requirements defined by the process planner. But what is

the effect of using that kind of aggregated and statistically constrained

information as an evaluation criterion for manufacturing processes where

the output not only depends on probability but on dependencies and

predictable changes?

One result in the appended publications (Bagge, et al., 2014) is that

there is a potential to get better utilisation of the manufacturing process

performance by sidestepping the established use of PCI, without

increasing quality related deviations.

As trends and non-normal distributed outputs can be recognised, not

only for the IPW but for dimensions on the final part, the traditional use

of PCI for FDs can be questioned also. This is also put in question by Wu,

et al., (2009). The aim with PCI is to prevent poor quality by an indirect

limit on the number of defects. The better the information is about

process behaviour then better are the possibilities to predict the outcome

and evaluate the risk of defects. Instead of traditional PCIs the use of

estimated DPMO is more appropriate because it does not require

normally distributed data.

On the other hand, better the process knowledge, better the

possibilities to control the process. This will reduce the impact from

systematic errors and the random errors will dominate. This implies that

the process output tends to be more normally distributed and the use of

PCIs more appropriate.

As discussed in section 2.5, the desirable balance between product

performance and process performance can be expressed as some kind of

PCI. A target value (not lower limit) for PCI, or better DPMO, should be a

way for the process planner to design a well balanced manufacturing

process. It is very important to find out the effect of using PCI in every

process planning case, both regarding IPW tolerances and final part

tolerances, to avoid low PUR.

6 Production Part Approval Process (PPAP) and Advanced Product Quality Planning (APQP) are

published by the Automotive Industry Action Group (AIAG) at www.aiag.org.


6.6 Relevance of the chosen problems to solve

During the work there have arisen some questions and hesitations

related to the very broad range of activities that can be associated with

process planning. One question is about the relevance of the identified

problems to solve and the chosen objectives for the thesis.

The first objective treated in this thesis has its origin in needs

identified by myself but is not for what either process planners in

industry or researchers usually ask.

Process planners tend to be aware of time-consuming, regularised,

activities that have to be done but do not require much process planning

skill, like creation of work instructions and NC programming. This kind of

activity can often be easily described and explained, but the decision-

making behind it cannot. Probably this is why industry promotes

supporting methods for these quite simple, but time-consuming,

activities instead of the more abstract process planning core business.

Academic research has in turn mainly been focusing on computer

automated process planning, including optimisation, more than

computer aided process planning in general terms. Even if many of the

research results are promising, there is a problem to introduce them into

an industrial process planning environment where few formal methods

are defined, let alone standardised, and decision-making is a matter of

personal experience and tacit knowledge.

Process planners probably do not see how they should integrate the

developed tools into their own work because of many undefined

interfaces between activities and no possibility for plug-and-play. It is a

risk that the effort of implementation of such tools is too high in relation

to the perceived potential. The “perceived potential” is in turn a

consequence of the process planner’s view of the overall scope of process

planning and priorities, eventually underpinned by company key

performance indicators (KPI).

The full extent of a process planning scope depends on not only the

part design, workshop facilities and new manufacturing technology but

also the process planning work itself. Different decisions will result in

more or less need for changes of the part design, rearrangements in the

workshop or investments in new technology. As long as process planning

decision-making is mostly heuristic and dependent on humans the

decisions will be different in spite of the fact that the prerequisites were

exactly the same. To understand the intention of the process planner at a


certain moment and make it possible to call a decision into question,

there must be a proper representation of the basis for that decision. The

records must be comprehensible, not only for a process planner but to

provide further academic research.

Even though most industrial process planners and academic

researchers have not prioritised comprehensible process planning

methods, the topic is still desirable and much work has to do be done

within this area before CAPP can make its breakthrough. Ham (1988) did

ask for more research facilitating a deeper understanding of process

planning tasks 25 years ago and more currently, Xu et al. (2011) invites

industry to play a more significant role in CAPP research.

After all, the chosen problems to solve have their relevance both for

academia and process planners in industry.

6.7 Scientific contribution

As described in chapter 3.2, the unsuccessful implementation of CAPP

in industry seems to be a consequence of a gap between industrial process

planning practice and the academic research. Despite asking for both

deeper understanding of process planning tasks (Ham, 1988) and a

“scientifically rigorous foundation for planning methods” (ElMaraghy,

1993) for more than two decades, this gap is still un-bridged.

The research presented in this thesis provides both the framework of

process planning methods and the refined PPP as contributions to the

establishment of a scientific foundation for process planning methods,

aimed to fill the gap.

The proposed framework of methods and the systematic

representation of the process planning process, provided by the refined

PPP, will also be an indirect support for future efforts in the development

and implementation of CAPP systems.

6.8 General application of the research results

The platform for the research has been manufacturing processes for

gear transmission parts as discussed in section 2.6. Gear manufacturing

processes have some special characteristics associated to the gear, but in

general there are many similarities to manufacturing of other mechanical

elements for engines, pumps, bearings, etc.

Yet no manufacturing processes have been identified that would not

be possible to develop by using the proposed methodologies. The PPP and


the triangular framework are supposed to be applicable in most

mechanical precision manufacturing environments, especially where

complex process chains appear.

In parallel to the scientific contribution, the research results have a

good potential to bring industrial process planning practice one step

forward. Both a manufacturing process designed by more ingenious

methods and the process planning work itself will gain better resource

utilisation in terms of PUR and project lead time respectively. In addition,

the potential to provide the product designer with relevant manufacturing

information is of great interest.

6.9 Conclusions

As process planning has a decisive impact on product quality and a

considerably high influence on product cost, the possibilities to efficiently

design and evaluate a process plan is of great interest.

This thesis has treated the realisation of process plan design and

evaluation by presenting several key contributors in the form of

systematic methods.

By gathering these methods into the triangular framework, it is

possible to cover the process planning domain from initial process

planning to prediction of the process outcome and finally establishing a

process plan based on transparent and well scrutinised decisions. This

corresponds very well to the first research objective.

It has also shown that each subject contributes with necessary

information for the rest of the framework, including the three

collaborating parties; product design, workshop facilities and

manufacturing technology.

The second objective is satisfied by the refined PPP that provides a

systematic representation of the process planning process. The refined

PPP is a contribution to knowledge about the essence of manufacturing

process planning.

Three methods for capture of process behaviour have been discussed.

One or more of these methods can be used to investigate process

behaviour and bring valuable input data to the development of the

process plan. The characteristics of each method are summarised in

Table 4.


Table 4: Comparison of three methods to capture process behaviour.

Simulations Design of experiments

Multivariate analysis

When? From pre-development to mature process stages

From early development stages when test equipment is available

Established processes

How? Assign FEM and/or analytical process models

Structured experiments, regression analysis

Gather production data, regression analysis

Resources Software for appropriate modelling. Data base with for example material properties.

Process knowledge and test equipment

Process knowledge production data

Model Process model Product model

Process model Process model

Use Simulation of process behaviour and prediction of process outcome. Prediction of secondary product properties.

Simulation of process behaviour and prediction of process outcome.

Simulation of process behaviour and prediction of process outcome.

Comment Some processes are difficult to model. Requires deep knowledge about the mechanisms behind the process behaviour.

Requires some process knowledge and free time in test equipment

Requires large quantities of high quality production data

65

7 Future work

7.1 Economic aspects

Despite cost being an important aspect when developing a

manufacturing process it has only been discussed in general terms in this

thesis. There is a need to combine the technical aspects of process

planning with the economic aspect in a systematic way. The PPP and

triangular framework adopt the important approach of covering the

complete manufacturing process chain. They facilitate evaluation of how

the aggregated technical contributions from the process steps involved

affect the final part. This must be followed by an analysis of the

aggregated economic contributions to the final manufacturing cost.

One approach for this should be to combine the methods within the

triangular framework with the technical-economic model described by

Ståhl (2007), further developed and implemented by Jönsson, et al.,

(2008a; 2008b). If this integration can be done, it will be possible to

define procedures for optimisation with an objective function on cost.

Another approach is to use system dynamics cost simulations in a

similar way to that Storck and Lindberg (2008) described for industrial

production in a steel plant.

7.2 Representation of the PPP and information flows

The representation of the triangular framework, the PPP and the

information flows are made in a simple way in this thesis to enhance

comprehensibility. However, a representation of activities, information

flows, etc. following a standard such these in the family of IDEF7

modelling techniques, should facilitate easier transfer of the results into

new research and development projects, especially in the context of

computer modelling.

7 www.nist.gov

66 | FUTURE WORK

7.3 The model driven approach

With some exceptions, CAD, CAM and CAPP solutions have not been

used to support the proposed process planning methods. There are many

well-established CAD and CAM systems on the market but their abilities

to manage process planning related characteristics are limited. The

benefit of CAM systems is the aid for creation and visualisation of NC

programs for single machine tools, but not for linked multi-step

manufacturing processes discussed in this thesis.

A challenge for production research is to bridge the gap between CAD

and CAM with systems capable to realize what has been presented in this

thesis, most probably called CAPP. By adopting the research results in

this thesis, it seems to be possible to develop a model driven approach, to

establish a base for representation of process planning related

information. Hedlind (2013) discusses the application of models to

create, represent and use information of products, manufacturing

processes and resources to support process planning. Further model

based approaches with good potential are these for machining tolerance

analysis discussed by Najad, et al. (2012). These approaches in

combination with the proposed methods and structures for process

planning presented in this thesis is suggested to be a subject for future

research, heading for usable CAPP systems.

7.4 A new approach for P-FMEA

Process failure mode and effects analysis (P-FMEA) is a step-by-step

procedure to identify and evaluate risks of failure in a manufacturing

process. The process is analysed regarding potential failures, the severity

of the failures and the effect of the failures.

As process planning is to define a process with a predictable outcome,

the risk assessment should be a part of this work. The DDC2 facilitates

representation of the process chain and has been used to analyse linked

tolerances and process behaviour propagation. The DDC2 also has a

potential to form a basis for process failure analysis by using the same

structure as for tolerance analysis. The information needed for such

assessment must therefore be integrated into the PPP and the DDC2

extended to support this kind of analysis.

67

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