UNIT IV

Quality:

Quality is not merely the goodness or otherwise of a finished product. It is the ultimate

objective of the company and is what customers expect from a product.

Some definitions of quality have been given

Quality is the conformance to specifications and standards.

Quality is the fitness for use.

Quality means productivity, competitive costs, timely delivery and total customer

satisfaction.

Hence a quality product should meet the following requirements.

Quality raw material should be used

Quality parts should be used

Should adopt quality process and processing conditions.

Should conform to standards.

Quality services.

Quality Control:

Quality control is the process through which the actual quality characteristics of the product

are measured and the performance of the manufacturing system (like machines, materials etc.,) is

monitored with an aim of comparing it with the standards, to take corrective action if there is any

deviation.

Objective of Quality Control:

Evaluation of quality standards of incoming material, product in manufacture and outgoing

product.

Evaluation of optimal quality attainable under given conditions.

To improve quality and productivity by process control and experimentation.

Developing procedures for good vendor – vendee relationship.

Stages of Quality Control:

The development of quality control is explained by the three stages of quality control namely :

Inspection

Statistical quality control (SQC)

Reliability.

Inspection:

Inspection is a part of quality control.

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Inspection is the checking of incoming materials, materials is process and finished products in

comparison with predetermined specifications, with the use of inspection instruments, for

detecting and sorting out the faulty or defective items.

Statistical Quality Control (SQC):

SQC is a method of estimating the quality of the whole from the quality of the samples taken

from the whole. The method is based upon the laws of chance and has a sound mathematical

base.

In statistical quality control, statistical tools techniques are employed to monitor and control

the quality or to solve problems related to quality.

Reliability:

Reliability is defined as the “Probability of performing without failures, a specified function under

given condition for a specified period of time”.

Introduction about Statistical quality control

TQM focuses on customer-driven quality standards, managerial leadership, continuous improvement,

quality built into product and process design, quality identified problems at the source, and quality

made everyone’s responsibility.

However, talking about solving quality problems is not enough. We need specific tools that can help us

make the right quality decisions.

These tools come from the area of statistics and are used to help Identify quality problems in the

production process as well as in the product itself.

Statistical quality control (SQC) is the term used to describe the set of statistical tools used by quality

professionals. Statistical quality control can be divided into three broad categories:

1. Descriptive statistics are used to describe quality characteristics and relationships. Included are statistics

such as the mean, standard deviation, the range, and a measure of the distribution of data.

2. Statistical process control (SPC) involves inspecting a random sample of the output from a process and

deciding whether the process is producing products with characteristics that fall within a predetermined range.

SPC answers the question of whether the process is functioning properly or not.

3. Acceptance sampling is the process of randomly inspecting a sample of goods and deciding whether to

accept the entire lot based on the results. Acceptance sampling determines whether a batch of goods should be

accepted or rejected.

Benefits of Statistical Quality Control (SQC):

SQC leads to effective inspection due to the inspection of samples which eliminates 100

percent inspection (which induces human fatigue causing errors).

SQC reduces the cost of inspection as acceptance or rejection of a lot of products is based on

sample inspection results.

SQC eliminated bottlenecks in the process of manufacturing.

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SQC assures quality of incoming and outgoing materials and products with less use of

resources.

Use of the concepts of SQC improves the quality of the process and the product without 100

percent inspection.

As SQC techniques detect faults at each and every stage of the product being manufactured.

Increase in productivity is directly seen due to reduction in waste of resources.

Limitations of Statistical Quality Control (SQC):

The employees of an organization have to be trained to use the SQC concepts and methods

Lot of paper work will be involved due to the introduction of SQC techniques which binds the

management to increase quality personnel.

At the introduction stage of SQC, the techniques used will indicate a number of problems to

be tackled in the organization. Due to this the employees will face a tough time initially.

All SQC techniques indicate whether or not the process is in control. It is purely left to the

management and users of these techniques to take quality decisions to overcome the indicated

problems.

Inspection

Inspection is defined as “the process of measuring, examining, testing, gagging, or otherwise

comparing the unit with the applicable requirements”.

Inspection essential quality tool is used in every organization to ensure that the quality of their

product is acceptable to the customer as well as the industry.

There are two primary purposes for inspection.

o These are to make sure the product conforms to specifications and to determine

whether a non-conforming product is fit for use.

Uses and Its Applications

Organizations may use different strategies for inspection depending upon the type of process

they use and the cost-effectiveness of using that strategy.

Types of inspection include

o Operator inspection,

o In-process inspection,

o Tollgate inspection,

o Automated inspection,

o 100 percent on-line inspection, and

o Computer-aided inspection.

Difference b/w inspection and quality control

Usually there is no difference. But both terms have slightly different meaning in different

circles. 3

Inspection means checking the characteristics of a product to ensure that conformity to a set of

specifications is met. Sometimes it means checking 100% of a batch of product; sometimes it

means checking only some samples (in that latter case, it is exactly the same as "statistical

quality control".

Quality control usually means only checking the conformity of products already made. It comprises

inspection and other tests such as lab tests. Some people use quality control to designate some more

upstream activities that aim at preventing quality issues.

Control charts

Introduction

It has become a latest trend today to purchase with high quality. This is because of the

increase in the literacy level of customers.

Hence it becomes necessary for the manufacture to produce products with high quality. It is

possible to produce high quality products by

Inducing quality in the process being adopted for manufacturing.

Adopting a process of manufacturing which is repeatable.

For the above mentioned purposes, the subject of statistical quality control was introduced

into the field of engineering and technology.

The control charts are widely being used in all types of industries throughout the world to

improve the quality of the product and also to reduce certain unwanted cost.

Objective of control charts

To constantly monitor a process to find whether it is in control or not.

To improve existing production procedures.

To establish new specifications for products which do not have them.

To establish and if necessary alter production procedure of any process.

To determine and eliminate the assignable causes.

To determine whether it is required to make some fundamental changes in production

methods adopted.

To reduce the cost involved in inspection.

Variable Data and Attribute Data:

Variable Data:

Whenever the quality characteristic is measured actually by the use of inspection instruments

and recorded, the quality is said to be expressed by variables.

Example: The diameter of a shift is measured and recorded as follows:

25.310 mm

Attribute Data: 4

When a record shows only the number of articles conforming and the number of articles

failing to conform to any specified requirements, it is said to be a record by attribute.

Example:

Number of items of conforming or non-conforming to the specification by the use of GO and

NO-GO gauges.

By using GO and NO-GO gauges, the articles inspected may be accepted if they are

conforming to the specifications (or) may be rejected if they are not conforming to the specifications.

Types of Control Charts:

Control charts may be classified into two general categories depending upon the quality

characteristic used for process control.

Some quality characteristics can be measured as variable data and some other quality

characteristics can be only differentiated as either conforming or non-conforming to given

specifications.

So, the two general types of control charts based on quality characteristics are.

Control charts for variables.

Control charts for attributes.

Generally, control chart is the simplest statistical technique and are used in on-line statistical process

control.

Control Charts

Control chart for variables.

X- R chart

X- S chart

control chart for attributes

p- chart

np- chart

c- chart

u-chart

Advantages of control charts

Control charts are the simplest graphical devices used to control quality. It is suitable for any type of

process, product and industry.

Product quality is improved.

It reduces spoilage of products or materials

Rework of products is reduced.

Cost of the product will be reduced due to savings in inspection cost.

It is used to find the causes of variation. 5

It is very much useful in standardization of inspection method.

Charts serve as monitoring device to monitor the performance of machines and operator.

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Acceptance Sampling

Acceptance sampling, the third branch of statistical quality control, refers to theprocess of

randomly inspecting a certain number of items from a lot or batch in order to decide whether

to accept or reject the entire batch.

What makes acceptance sampling different from statistical process control is that acceptance

sampling is per-formed either before or after the process, rather than during the process.

Acceptance sampling before the process involves sampling materials received from a supplier,

such as randomly inspecting crates of fruit that will be used in a restaurant, boxes of glass

dishes that will be sold in a department store, or metal castings that will be used in a machine

shop.

Sampling after the process involves sampling finished items that are to be shipped either to a

customer or to a distribution canter. Examples include randomly testing a certain number of

computers from a batch to make sure they meet operational requirements, and randomly

inspecting snowboards to make sure that they are not defective. 24

Acceptance sampling terms

Acceptable Quality levels (AQL)

Number of defect percentage allowed in a lot which can still be considered accepted(Type I

error)

Lot Tolerance Percent Defective(LTPD)

Amount of defects that will come with a lot of goods(Type II error)

Sampling Plan

Forms after n and c values have been found

Producer’s risk

Risk associated with a lot of acceptable quality rejected

Consumer’s risk

Receive shipment, assume good quality, actually bad quality

N

Sample size taken for your sampling plan

C

Where rejections would occur when defects exceeded this percent

Operating characteristics curve (OC)

A graph, displaying standards at which shipments would be accepted

Purpose of Acceptance sampling

– Determine the quality level of an incoming shipment or, at the end production

– Ensure that the quality level is within the level that has been predetermined

Acceptance sampling Method

ACCEPTANCE SAMPLING

ACCEPT LOT

RETURN LOT TO SUPPLIER 100% INSPECTION

DECISION

REJECT LOT SAMPLE AGAIN

DECISION

INSPECT SAMPLE

Type title here

TAKE SAMPLE

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Acceptance sampling advantages

Less handling damages

Fewer inspectors to put on payroll

100% inspection costs are to high

100% testing would take to long

Acceptance sampling disadvantages

Risk included in chance of bad lot “acceptance” and good lot “rejection”

Sample taken provides less information than 100% inspection

Sampling Plan

A sampling plan is a plan for acceptance sampling that precisely specifies the parameters of

the sampling process and the acceptance/rejection criteria. The variables to be specified include the

size of the lot (N), the size of the sample inspected from the lot (n), the number of defects above

which a lot is rejected (c), and the number of samples that will be taken.

There are different types of sampling plans.

Single sampling plan

Double sampling plan

Multi sampling plan

Single sampling:

In which a random sample is drawn from every lot. Each item in the sample is examined and

is labeled as either“good”or“bad”.Depending on the number of defects or“bad”items found, the entire

lot is either accepted or rejected. For example, a lot size of 50 cookies is evaluated for acceptance by

randomly inspecting 10 cookies from the lot. The cookies may be inspected to make sure they are not

broken or burned. If 4or more of the 10 cookies inspected are bad, the entire lot is rejected. In this

example, the lot size N=50, the sample size n=10, and the maximum number of defects at which a lot

is accepted isc =4. These parameters define the acceptance sampling plan.

Double sampling:

Another type of acceptance sampling is called double sampling. This provides an opportunity

to sample the lot a second time if the results of the first sample are in conclusive. In double sampling

we first sample a lot of goods according to per cent criteria for definite acceptance or rejection.

However, if the results fall in the middle range, they are considered inconclusive and a second sample

is taken. For example, a water treatment plant may sample the quality of the water ten times in

random intervals throughout the day. Criteria may be set for acceptable or unacceptable water quality,

such as .05 per cent chlorine and .1 per cent chlorine. However, a sample of water containing between

.05 per cent and .1 per cent chlorine is inconclusive and calls for a second sample of water. 26

Multiple sampling:

In addition to single and double-sampling plans, there are multiple sampling plans.

Multiple sampling plans are similar to double sampling plans except that criteria are set for more than

two samples. The decision as to which sampling plan to select has a great deal to do with the cost

involved in sampling, the time consumed by sampling, and the cost of passing on a defective item. In

general, if the cost of collecting a sample is relatively high, single sampling is preferred. An extreme

example is collecting a biopsy from a hospital patient. Because the actual cost of getting the sample is

high,we want to get a large sample and sample only once. The opposite is true when the cost of

collecting the sample is low but the actual cost of testing is high.

Operating Characteristic (OC) Curves

As we have seen, different sampling plans have different capabilities for discriminating

between good and bad lots. At one extreme is 100 percent inspection, which has perfect

discriminating power. However, as the size of the sample inspected decreases, so does the chance of

accepting a defective lot. We can show the discriminating power of a sampling plan on a graph by

means of an operating characteristic (OC) curve. This curve shows the probability or chance of

accepting a lot given various proportions of defects in the lot.

Figure 6-11 shows a typical OC curve. The x axis shows the percentage of items that are

defective in a lot. This is called“lot quality”. They axis shows the probability or chance of accepting a

lot. You can see that if we use 100 per cent inspection we are certain of accepting only lots with zero

defects. However, as the proportion of defects in the lot increases, our chance of accepting the lot

decreases. For example, we have a 90 per cent probability of accepting a lot with 5 per cent defects

and an 80 per cent probability of accepting a lot with 8 per cent defects.

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Regardless of which sampling plan we have selected, the plan is not perfect. That is, there is

still a chance of accepting lots that are“bad “and rejecting “good” lots. The steeper the OC curve, the

better our sampling plan is for discriminating between“good”and“bad”.Figure 6-12 shows three

different OC curves, A, B, and C. Curve A is the most discriminating and curve C the least. You can

see that the steeper the slope of the curve, the more discriminating is the sampling plan. When100 per

cent inspection is not possible; there is a certain amount of risk for consumers in accepting defective

lots and a certain amount of risk for producers in rejecting good lots.

There is a small percentage of defects that consumers are willing to accept. This is called the

acceptable quality level (AQL)and is generally in the order of 1–2 per cent. However, sometimes the

percentage of defects that passes through is higher than the AQL. Consumers will usually tolerate a

few more defects, but at some point the number of defects reaches a threshold level beyond which

consumers will not tolerate them. This threshold level is called the lot tolerance per cent defective

(LTPD).The LTPD is the upper limit of the percentage of defective items consumers are willing to

tolerate.

Consumer’s risk is the chance or probability that a lot will be accepted that contains a greater

number of defects than the LTPD limit. This is the probability of making a Type II error that is,

accepting a lot that is truly “bad”. Consumer’s risk or Type II error is generally denoted by beta (ß).

The relationships among AQL, LTPD, and ß are shown in Figure 6-13.Producer’s risk is the chance

or probability that a lot containing an acceptable quality level will be rejected. This is the probability

of making a Type I error—that is, rejecting a lot that is “good”. It is generally denoted by alpha

(alpha).

We can determine from an OC curve what the consumer’s and producer’s risks are. However,

these values should not be left to chance. Rather, sampling plans are usually designed to meet specific

levels of consumer’s and producer’s risk. For example, one common combination is to have a

consumer’s risk (ß) of 10 per cent and a producer’s risk (alpha) of 5 per cent, though many other

combinations are possible.

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