Case study on Design optimization of cutting parameters

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Case study on Design optimization of cutting parameters for turning operations based on the

Case study on Design optimization of cutting parameters for turning operations based on the Taguchi method SUBMITTED TO : - SUBMITTED BY : - DR. JAGTAR SINGH JATINDER SINGH (PGMSE 1650125) (DEPTT. OF MECH. ENGG. ) SANDEEP KUMAR YADAV(PGMSE 1650129) LOVEPREET SINGH (PGMSE 1650181) KULWINDER SINGH LOTTE(PGMSE 1650186)

DOE - Taguchi Method q Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan

DOE - Taguchi Method q Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan has developed a method based on " ORTHOGONAL ARRAY " experiments. q This gives much reduced " variance " for the experiment with " optimum settings " of control parameters. q "Orthogonal Arrays" (OA) provide a set of well balanced (minimum) experiments serve as objective functions for optimization.

Introduction of Taguchi Method Ø It is a stastical approach to optimize the process

Introduction of Taguchi Method Ø It is a stastical approach to optimize the process parameter. Ø Improve the quality of manufacturing components. Ø Use of orthogonal array with lesser experiments. Ø Use of loss function to measure the performance characteristics that are deviating from the desired target value.

Concept of Orthogonal Array Designs Ø Basically a type of general fractional factorial design.

Concept of Orthogonal Array Designs Ø Basically a type of general fractional factorial design. Ø Select subset of combinations of multiple factors at multiple levels. Ø OA are balanced to ensured that all levels of all factors are considered equally.

Selection of Orthogonal Arrays(OA) The selection of which OA to use predominantly depends on

Selection of Orthogonal Arrays(OA) The selection of which OA to use predominantly depends on these items in order of priority: Ø The number of factors and interactions of interest Ø The number of levels for the factors of interest Ø The desired experimental resolution or cost limitations.

Orthogonal Array For orthogonal array their are two main factors use – q Factors

Orthogonal Array For orthogonal array their are two main factors use – q Factors A factor is a variable under study; an input that can be controlled. A factor can be either quantitative (measured in a continuous scale; example: memory utilization) or qualitative (measured in an integral scale example: radio button). q Levels A level is a value that a factor can assume when used in an experiment. For example, memory utilization should be less than 1 MB.

Orthogonal Array

Orthogonal Array

Standard Orthogonal Arrays L 4 Array L 9 Array L 8 Array

Standard Orthogonal Arrays L 4 Array L 9 Array L 8 Array

L 16 Array L 18 ARRAY

L 16 Array L 18 ARRAY

Case study on Design optimization of cutting parameters for turning operations based on the

Case study on Design optimization of cutting parameters for turning operations based on the Taguchi method q Introduction q Description of the Taguchi method q The turning process experiment q Design and analysis of cutting parameters q Concluding remarks q References

Introduction Ø Select cutting parameters for achieving high cutting performance. the desired cutting parameters

Introduction Ø Select cutting parameters for achieving high cutting performance. the desired cutting parameters are determined based on experience or by use of a handbook. To select the cutting parameters properly, to establish the relationship between the cutting performance and the cutting parameters. Ø Taguchi method provides a simple, efficient and systematic approach to optimize designs for performance, quality, and cost. Ø Taguchi method to determine and analyze the optimal cutting parameters are described next. The optimal cutting parameters with regard to performance indexes such as tool life and surface roughness are considered.

Introduction (cont…. . Fig. 1. Basic turning operation

Introduction (cont…. . Fig. 1. Basic turning operation

The turning process experiment Turning is machining process in which a single-point cutting tool

The turning process experiment Turning is machining process in which a single-point cutting tool removes material from the surface of a rotating cylindrical work piece. The cutting tool is fed linearly in a direction parallel to the axis of rotation. Three cutting parameters, need to be determined in a turning operation Ø Cutting speed Ø Feed rate Ø Depth of cut.

Selection of the cutting parameters and their levels Three levels of the cutting parameters

Selection of the cutting parameters and their levels Three levels of the cutting parameters are selected.

Design and analysis of cutting parameters PROCEDURE Ø An orthogonal array is used to

Design and analysis of cutting parameters PROCEDURE Ø An orthogonal array is used to reduce the number of cutting experiments for design optimization of the cutting parameters. Ø Results of the cutting experiments are studied using the S/ N and ANOVA analysis. Ø Based on the results of the S/N and ANOVA analyses, optimal settings of the cutting parameters for tool life and surface roughness are obtained and verified.

Orthogonal array experiment Ø To select an appropriate orthogonal array for the experiments, the

Orthogonal array experiment Ø To select an appropriate orthogonal array for the experiments, the total degrees of freedom need to be computed. Ø In an L 9 orthogonal array with 4 columns and 9 rows was used. This array has eight degrees of freedom and it can handle three-level design parameters. Ø The L 9 orthogonal array has four columns, one column of the array is left empty for the error of experiments: orthogonality is not lost by letting one column of the array remain empty.

Analysis of the S/N ratio

Analysis of the S/N ratio

Analysis of the S/N ratio(Cont….

Analysis of the S/N ratio(Cont….

Analysis of the S/N ratio of tool life(Cont….

Analysis of the S/N ratio of tool life(Cont….

S/N Ratio calculation of Tool life Details Since the objective function (Tool life) is

S/N Ratio calculation of Tool life Details Since the objective function (Tool life) is Higher-the-better type of control function, was used in calculating the S/N ratio.

S/N Ratio calculation of Tool life Details Ø The S/N ratio for the individual

S/N Ratio calculation of Tool life Details Ø The S/N ratio for the individual control factors are calculated as given below: Ø Ss 1=(η₁+η₂+η 3), Ss 2=(η 4+η 5+η 6) & Ss 3=(η 7+η 8+η 9) Ø Sf 1=(η₁+η 4+η 7), Sf 2=(η 2+η 5+η 8) & Sf 3=(η 3+η 6+η 9) Ø St 1=(η₁+η 5+η 9), St 2=(η 2+η 6+η 7) & St 3=(η 3+η 4+η 8) For selecting the values of η₁, η 2, η 3 etc. and to calculate Ss 1, Ss 2 & Ss 3. ηk is the S/N ratio corresponding to Experiment k.

S/N Ratio calculation of Tool life Details

S/N Ratio calculation of Tool life Details

of the S/Nofratio surface S/NAnalysis Ratio calculation Tooloflife Details roughness

of the S/Nofratio surface S/NAnalysis Ratio calculation Tooloflife Details roughness

Analysis of the S/N ratio of surface roughness Since the objective function (surface roughness)

Analysis of the S/N ratio of surface roughness Since the objective function (surface roughness) is smaller-the-better type of control function, was used in calculating the S/N ratio.

Analysis of the S/N ratio of surface roughness Since the objective function (Surface Finish)

Analysis of the S/N ratio of surface roughness Since the objective function (Surface Finish) is smaller-the-better type of control function, was used in calculating the S/N ratio. The S/N ratio for the individual control factors are calculated as given below: Ss 1=(η₁+η₂+η 3), Ss 2=(η 4+η 5+η 6) & Ss 3=(η 7+η 8+η 9) Sf 1=(η₁+η 4+η 7), Sf 2=(η 2+η 5+η 8) & Sf 3=(η 3+η 6+η 9) St 1=(η₁+η 5+η 9), St 2=(η 2+η 6+η 7) & St 3=(η 3+η 4+η 8) For selecting the values of η₁, η 2, η 3 etc. and to calculate Ss 1, Ss 2 & Ss 3. ηk is the S/N ratio corresponding to Experiment k.

Analysis of variance

Analysis of variance

Analysis of variance

Analysis of variance

Analysis of variance

Analysis of variance

Analysis of variance References for surface roughness

Analysis of variance References for surface roughness

Conclusion q This experiment has discussed an application of the Taguchi method for optimizing

Conclusion q This experiment has discussed an application of the Taguchi method for optimizing the cutting parameters in turning operations. q The tool life and surface roughness can be improved significantly for turning operations. q Taguchi method provides a systematic and efficient methodology for the design optimization of the cutting parameters with far less effect than would be required for most optimization techniques. q The improvement of tool life and surface roughness from the initial cutting parameters to the optimal cutting parameters is about 250%.

Reference [1] A. M. Abuelnaga, M. A. El-Dardiry, Optimization methods for metal cutting, Int.

Reference [1] A. M. Abuelnaga, M. A. El-Dardiry, Optimization methods for metal cutting, Int. J. Mach. Tool Des. Res. 24 (1) (1984) 1– 18. [2] P. L. B. Oxley, Modelling machining processes with a view to their optimization and the adaptive control of metal cutting machine tools, Robot. Comput. -Integrated Manuf. 4 (1988) 103– 119. [3] G. Chryssolouris, M. Guillot, A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining, ASME J. Eng. Ind. 112 (1990) 122– 131. [4] Y. Yao, X. D. Fang, Modelling of multivariate time series for tool wear estimation in finish turning, Int. J. Mach. Tools Manuf. 32 (4) (1992) 495– 508. [5] C. Zhou, R. A. Wysk, An integrated system for selecting optimum cutting speeds and tool replacement times, Int. J. Mach. Tools Manuf. 32 (5) (1992) 695– 707. [6] M. S. Chua, M. Rahman, Y. S. Wong, H. T. Loh, Determination of optimal cutting conditions using design of experiments and optimization techniques, Int. J. Mach. Tools Manuf. 33 (2) (1993) 297– 305. [7] G. Taguchi, Introduction to Quality Engineering, Asian Productivity Organization, Tokyo, 1990. [8] P. J. Ross, Taguchi Techniques for Quality Engineering, Mc- Graw-Hill, New York, 1988.