Optimization of Boring Parameters

V Sai Nitish Amit Shiva Kumar Chiranjeevi Chennu

UNDER THE GUIDANCE OF Dr. BADARI NARAYANA KANTHETI

BUCKET ORIELS SPECIFICATIONS AND IMPORTANCE

• Bucket oriels are welded supports which act as a connection between the arm of the earth moving machine and the bucket which carries the load.

• Bucket oriels bear huge amounts of load, which makes it one of the most crucial components of the earth moving machine.

• Minute changes in diameter and surface finish might result in excess friction, vibrations, wear and tear.

BUCKET ORIELS ARE ATTACHED AT THE BACK OF THE BUCKET BY METAL INERT GAS WELDING

Objectives

• To obtain surface finish of the bore to be less than 2 micro meters

• To achieve a dimensional conformance of diameter of the hole of 70 mm.

• To find the parameters influencing the boring operation.

• In many manufacturing organizations experiments are performed to increase the understanding and knowledge of various manufacturing processes. For continuous improvement in product/process quality, it is fundamental to understand the process behaviour, the amount of variability and its impact on product / processes. The present work being optimization of boring parameters, the literature related to this subject area is briefly covered.

• The study of cutting forces of boring process is important for selecting appropriate proper cutting conditions. A predictive cutting force model has been developed and influence of cutting parameters on force components has been studied by Alwarsamy et al., 2011. In their work a new analytical cutting force model obtain from the information related to the work piece and tool signature for a boring operation.

• The optimization work done by Gaurav Vohra et al 2013, of the boring parameters for a CNC

turning centre such as speed, feed rate and depth of cut on aluminium to achieve the highest possible Material removal rate and at minimum surface roughness by using the Taguchi method. In their work it was found that the depths of cut and cutting speed to be the most influencing parameter, followed by the feed rate.

• Kanase Sandip et al 2013 proposed an innovative method to reduce tool chatter and enhance surface finish in boring operation. Their results showed the use of passive damping technique has vast potential in the reduction of tool chatter.

Literature Review

• The optimum conditions to obtain better damping in boring process for chatter reduction is identified for various cutting conditions by Prasanna venkadesan et al, 2015.

• Lee and Tarng 2001 used computer vision techniques to inspect surface roughness of a work piece under a variation of turning operations and developed a relationship between the feature of the surface image and the actual surface roughness under a variation of turning operations for predicting surface roughness with reasonable accuracy.

• Davim 2003 studied the influence of cutting condition and cutting time on turning metal

matrix composites and used Taguchi method and (ANOVA) for the analyses. They obtained a correlation between cutting velocity, feed and cutting time with tools wear, the power required and the surface roughness of the job.

• Balamuruga and Sugumaran 2013 developed a Gaussian process regression model to predict the surface roughness based on tool post vibration and cutting conditions. In their work the data obtained from their experiments and collected from previous studies, have been used to validate the model.

• Badadhe et. al., 2012, optimized combination of cutting parameters to achieve the

minimum value of surface roughness. In their work, the experimentation was carried out with the help of lathe machine, with three machining parameters such as spindle speed, feed and depth of cut taken as control factors. During the machining, the boring bar is subjected vibrations in different directions; the impact of these vibrations on the surface roughness which is an important quality parameter of the bored surface was estimated.

Analysis and Optimization of Parameters Affecting SurfaceRoughness in a Boring Process using DOE

• Manufacturing industries are continuously demanding for higher production rate and improved machinability as quality and productivity play significant role in the manufacturing environment.

• Higher production rate can be achieved at high cutting speed, feed and depth of cut

• The quality of the final product is limited by capability of tooling and surface finish.

• Over all performance of the operation depends on the selection of right cutting parameters and is generally a compromise between several variables.

• Depth of cut at 1 mm (1 level) • Feed rate is varied at 40, 80, 120, 160, 200 and 240 in mm/min ( 6

levels)• Speed of cut 800, 900 and 1200 rpm ( 3 levels) • The number of possible iteration using a full factorial will be 6*3 =

18. In the present program, as iterations required are small all possible combinations are used.

• The responses measured for each iteration are: a) surface roughness (RA) and b) hole diameter.• To analyse the data , a statistical tool DESIGN OF

EXPERIMENTS (Mini TAB Ver 16) is used to arrive at proper improvement plan of the manufacturing process parameters. The experimentation plan is designed using design of experiment (DOE).

• It is possible to determine these parameters using ANOVA, Response Surface Methodologies including optimization.

THE PLAN

A sample data of inputs and response parameters is shown below

Sl. No Factor 1Depth of cut , mm

Factor 2Feed rate

mm/min

Factor 3 Speed of cut

RPM

Surface finish RA

µ m

Diameter of hole, mm

1 1 40 800 2.320 70.025

2 1 40 900 2.306 70.010

3 1 80 900 1.618 70.015

4 1 80 1200 1.968 70.000

. . . . . .

. . . . . .

18 1 120 1200 2.306 69.999

SAMPLE

EQUIPMENT USED FOR BORING

Tool holder

TOOLS

JIGS & FIXTURES

MX-5 MACHINE

CONTROL UNIT

EQUIPMENT USED FOR BORING

TOOL HOLDER

AUTOMATIC TOOL CHANGER CONTROL UNIT

Calibration of surface tester

Surface Roughness Tester LC : 1 nm

MEASURING INSTRUMENT USED

A Mitutoyo SJ-201P apparatus was used. For

each specimen three readings were taken at

approximately 1200 angles apart and the average value

was used for the subsequent analysis.

BORE GAUGELC: 1 MICRON

Bore Gauge

Dial

Attachments

DOE ANALYSIS IS DONE USING MINITAB VER 16.0

Steps involved in statistical analysis:

1. Statistical analysis -> DOE -> Factorials -Table of influencing factors will be generated

2. Statistical analysis -> ANOVA -> Balanced ANOVA -> Main Effect plot and Interaction Effect Plot

3. Statistical analysis -> DOE -> Response Surface -> Surface and Contour plots

4. Statistical analysis -> DOE -> Selection of optimal design -> Response optimization

5. Model validation -> Use optimal factors, repeat the boring operation and measure the respective hole diameter and surface roughness

Full Factorial Design • Full factorial designs include all possible combinations of every

level of every factor.

• Full factorial designs can require a lot of trials, which can take a lot of time and cost.

• Requires at least one observation for every combination of factors and levels.

• Allows for the measurement of all possible interactions.

• Ex1. a design of 4 factors with 3 levels each would be: 3 x 3 x 3 x 3 = 3^4 = 81 observations.

• Ex 2. 4 factors (A=3, B = 2, C=5, D= 4 levels). 3 x 2 x 5 x 4 = 120 observations.

TABLE REPRESENTING FULL FACTORIAL DATA

SPEED(RPM)

DEPTH OFCUT mm

FEED RATEmm/min

HOLE DIAMETERmm

SURFACE FINISH (RA) µ m

800 1 40 70.025 2.320

900 1 40 70.010 2.306

1200 1 40 70.015 1.618

800 1 80 70.000 1.968900 1 80 69.999 1.954

1200 1 80 70.025 1.709

800 1 120 70.015 2.766

900 1 120 70.020 2.306

1200 1 120 70.020 1.742

800 1 160 70.015 2.616

900 1 160 70.020 2.566

1200 1 160 69.980 2.320

800 1 200 70.010 2.814

900 1 200 69.985 2.667

1200 1 200 69.980 2.592

800 1 240 70.012 4.902

900 1 240 70.006 3.306

1200 1 240 70.025 2.742

READINGS TAKEN FOR VARYING SPEED AND FEED RATE

Effect of speed and feed on surface finish

Effect of speed and feed on diameter

Surface plot for Surface Finish

Contour plot for Surface Finish

Surface plot for Diameter

Contour plot for Diameter

Optimization plot

Model Validation -> Use optimal factors, repeat the boring operation and measure the respective hole diameter and surface roughness

Optimal values Speed 1000 rpm , Feed 100 mm/min Expected Dia 70.005 mm and RA = 1.843 µ m

Sl.no RPM Feed Dia Ra edia ddia eRa dRa1 800 40 70.03 2.32 70.02 0.005 2.3098 0.012 900 40 70.01 2.306 70.016 -0.006 2.0103 0.2963 1200 40 70.02 1.618 70.022 -0.007 1.8553 -0.2374 800 80 70 1.968 70.015 -0.015 2.3068 -0.3395 900 80 70 1.954 70.01 -0.011 1.9751 -0.0216 1200 80 70.03 1.709 70.014 0.011 1.7232 -0.0147 800 120 70.02 2.766 70.011 0.004 2.4824 0.2848 900 120 70.02 2.306 70.006 0.014 2.1184 0.1889 1200 120 70.02 1.742 70.008 0.012 1.7698 -0.028

10 800 160 70.02 2.616 70.011 0.004 2.8366 -0.22111 900 160 70.02 2.566 70.004 0.016 2.4403 0.12612 1200 160 69.98 2.32 70.004 -0.024 1.9949 0.32513 800 200 70.01 2.814 70.012 -0.002 3.3694 -0.55514 900 200 69.99 2.667 70.005 -0.02 2.9409 -0.27415 1200 200 69.98 2.592 70.003 -0.023 2.3987 0.19316 800 240 70.01 4.902 70.016 -0.004 4.0808 0.82117 900 240 70.01 3.306 70.009 -0.003 3.62 -0.31418 1200 240 70.03 2.742 70.005 0.02 2.981 -0.239

19 1000 100 70.0045 1.711 70.005 0.0005 1.843 0.132

Confirmatory experimental valuesDia mm Ra µm

70.013 1.616

69.985 1.99970.015 1.517

Confidence interval ( with 95% confidence)

= mean +/- SD =70.0045+/- 0.0173

= 70.021, 69.987Predicted value

70.005 is with in the optimal settings hence the predicted

model is sound.

Validation Of Results

Upper limit = 2 µm

Avg value = 1.7609 µm

Target value = 70.00 mm

mm

Avg value = 70.0132 mm

Conclusion

In this project, we have selected the combination of optimum cutting parameters which will result in better surface finish and diameter of the bore. Three parameters viz. spindle speed, feed and depth of cut of the MX-5 machine were taken as control factors. The cutting trials were performed as per full factorial method to deal with the response from multi-variables. Carbon steel was used as a job material which was cut by using carbide tool. DOE and Analysis of Variance (ANOVA) was carried out to find the significant factors and their individual contribution in the response function i.e. surface roughness and diameter accuracy of the bore.

After the validation, we have determined that the most optimal values for our given machining conditions of speed, feed rate and depth of cut are 1000 rpm, 100 mm/min and 1 mm respectively.

Future scope of study

The selection of optimal cutting parameters to obtain specified surface roughness is very difficult for any work piece material. In the current scenario, desired surface roughness can be achieved by trial and error method. But it is time consuming and material is unnecessarily wasted for this purpose. So, there is a need to find a technique that can predict cutting parameters for the desired surface roughness.

Following are the recommendations for continuing the research work in this area.

1. Improved method for surface roughness measurement with higher sampling rate can be helpful to locate the principal frequencies of boring vibrations in the surface roughness spectrum.

2. In depth study of dynamic cutting force can be carried out regards to frequency content. 3. An intelligent machine controller can be developed as per the technique proposed in the present work for automatic adjustment of the machining parameters. 4. Detail analysis of time delays in control action and stability analysis of the control system can be carried out.

• S. Vetrivel et al., “Theoretical Cutting Force Prediction and Analysis of Boring Process Using Mathcad”, World Applied Sciences Journal 12 (10): 1814-1818, 2011

• Gaurav Vohra et al., “Analysis and Optimization of Boring Process Parameters by Using Taguchi Method”, International Journal of Computer Science and Communication Engineering IJCSCE Special issue on “Recent Advances in Engineering & Technology” NCRAET-2013.

• Kanase Sandip et. al., “Improvement Of Ra Value Of Boring Operation Using Passive Damper”, The International Journal Of Engineering And Science (IJES), Vol. 2, Iss 7, P 103-108, 2013.

• V. Prasanna Venkadesan, et al., “Chatter Suppression in Boring Process Using Passive

Damper”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering Vol: 9, No: 11, 2015.

• Yang WH, Tarng YS ,1998, “Design optimization of cutting parameters for turning operations based on the Taguchi method”, J. of Materials Processing Technology 84:122-129.

REFERENCES

• Davim, J.P., 2001, "A Note on the Determination of Optimal Cutting Conditions for Surface Finish Obtained in Turning Using Design of Experiments", Journal of Materials Processing Technology, Vol. 116, Iss. 23, pp. 305-308.

• Lee B.Y., Tarng Y.S., 2001, “Surface roughness inspection by computer vision in turning operations”, International Journal of Machine Tools & Manufacture 41 (2001): 1251

• Davim J. P., “Design of optimization parameters for turning metal matrix composites based on the orthogonal arrays”, Journal Processing Technology, 132, 2003, pp. 340

• Balamuruga Mohan Raj. G, Sugumaran. V, 2013, “Developing Gaussian Process Model to Predict the Surface Roughness in Boring Operation”, International Journal of Engineering Trends and Technology- Vol. 4 Iss.3, 2013

• A. M. Badadhe et. al., 2012, “Optimization of Cutting Parameters in Boring Operation”, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), P 10-15.

PUBLICATIONThis work has been accepted to be presented as a paper at International Conference on Recent Advances in Engineering Sciences (ICRAES) in MS Ramaiah Institute of Technology, Bangalore on 8th of September 2016.

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