Machining Parameters Optimization of AA6061 Using Response Surface Methodology and Particle Swarm Optimization

  • Rashmi Lmalghan
  • Karthik Rao M C
  • ArunKumar S
  • Shrikantha S. Rao
  • Mervin A. Herbert
Regular Paper
  • 14 Downloads

Abstract

The influence of cutting parameters on the responses in face milling has been examined. Spindle speed, feed rate and depth of cut have been considered as the influential factors. In accordance with the design of experiments (DOE) a series of experiments have been carried out. The paper exemplifies on the optimizing the process parameters in milling through the application of Response surface methodology (RSM), RSM-based Particle Swarm Optimization (PSO) technique and Desirability approach. These aforesaid techniques have been applied to experimentally establish data of AA6061 aluminium material to study the effect of process parameters on the responses such as cutting force, surface roughness and power consumption. By adopting the multiple regression techniques, the interaction between the process parameters are acquired. The optimal parameters have been found by adopting the multi-response optimization techniques, i.e. desirability approach and PSO. The performance capability of PSO and desirability approach is investigated and found that the values obtained from PSO are comparable with the values of desirability approach.

Keywords

Face milling Regression Particle swarm optimization Desirability Response surface methodology Design of experiment 

Nomenclature

AMMC's

Aluminium Matrix Composites

ANFIS

Adaptive Fuzzy Interface System

ANOVA

Analysis of variance

ANN

Artificial Neural Network

CCFCD

Central Composite Face Centred Design

CNC

Computer Numerical Control

DOE

Design of Experiment

FX

Cutting Force, N

GA

Genetic Algorithm

gbest

Global best

pbest

Particle best

GSA

Genetic simulated annealing

MMC

Metal Matrix Composite

PSO

Particle Swarm Optimization

RSM

Response Surface Methodology

R-sq

Pre R-squared

R-sq (adj)

Adj R-Squared

RGA

Real Parameters Genetic Algorithm

RPD

Relative percentage deviation

HGA

Hybrid Genetic Algorithm SQP

SGA

Simple Genetic Algorithm

SR

Surface roughness, μm

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Demir, H., and Gündüz, S., “The Effects of Aging on Machinability of 6061 Aluminium Alloy,” Materials & Design, vol. 30, no. 5, pp. 1480–1483, 2009.CrossRefGoogle Scholar
  2. 2.
    Dikshit, M. K., Puri, A. B., and Maity, A., “Empirical Modelling of Dynamic Forces and Parameter Optimization Using Teaching-Learning-Based Optimization Algorithm and RSM in High Speed Ball-End Milling,” Journal of Production Engineering, vol. 19, no. 1, pp. 11–21, 2016.Google Scholar
  3. 3.
    Bhopale, N. N., Joshi, S. S., and Pawade, R. S., “Experimental Investigation into the Effect of Ball End Milling Parameters on Surface Integrity of Inconel 718,” Journal of Materials Engineering and Performance, vol. 24, no. 2, pp. 986–998, 2015.CrossRefGoogle Scholar
  4. 4.
    Tandon, V., El-Mounayri, H., and Kishawy, H., “NC End Milling Optimization Using Evolutionary Computation,” International Journal of Machine Tools and Manufacture, vol. 42, no. 5, pp. 595–605, 2002.CrossRefGoogle Scholar
  5. 5.
    Tandon, V. and El-Mounayri, H., “A Novel Artificial Neural Networks Force Model for End Milling,” The International Journal of Advanced Manufacturing Technology, vol. 18, no. 10, pp. 693–700, 2001.CrossRefGoogle Scholar
  6. 6.
    Conceicao A. C. A., Castro, C., and Davim, J., “Optimisation of Multi-Pass Cutting Parameters in Face-Milling Based on Genetic Search,” The International Journal of Advanced Manufacturing Technology, vol. 44, Nos. 11–12, pp. 1106–1115, 2009.Google Scholar
  7. 7.
    Zhou, J., Ren, J., and Yao, C., “Multi-Objective Optimization of Multi-Axis Ball-End Milling Inconel 718Via Grey Relational Analysis Coupled with RBF Neural Network and PSO Algorithm,” Measurement, vol. 102, pp. 271–285, 2017.CrossRefGoogle Scholar
  8. 8.
    Saffar, R. J., Razfar, M., Salimi, A., and Khani, M., “Optimization of Machining Parameters to Minimize Tool Deflection in the End Milling Operation Using Genetic Algorithm,” World Applied Sciences Journal, vol. 6, no. 1, pp. 64–69, 2009.CrossRefGoogle Scholar
  9. 9.
    Patwari, M. A. U., Amin, A., and Arif, M. D., “Optimization of Surface Roughness in End Milling of Medium Carbon Steel by Coupled Statistical Approach with Genetic Algorithm,” Special Issue of the International Journal of the Computer, the Internet and Management, Vol. 19, No. SP1, pp. 41.1-41.5, 2011.Google Scholar
  10. 10.
    Gupta, A. K., Chandna, P., and Tandon, P., “Hybrid Genetic Algorithm for Minimizing Non Productive Machining Time during 2.5D Milling,” International Journal of Engineering, Science and Technology, vol. 3, no. 1, pp. 183–190, 2011.Google Scholar
  11. 11.
    Baskar, N., Asokan, P., Prabhaharan, G., and Saravanan, R., “Optimization of Machining Parameters for Milling Operations Using Non-Conventional Methods,” The International Journal of Advanced Manufacturing Technology, vol. 25, Nos. 11–12, pp. 1078–1088, 2005.CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Rahman, M., Wong, Y., and Sun, J., “Optimization of Multi-Pass Milling Using Parallel Genetic Algorithm and Parallel Genetic Simulated Annealing,” International Journal of Machine Tools and Manufacture, vol. 45, no. 15, pp. 1726–1734, 2005.CrossRefGoogle Scholar
  13. 13.
    Reddy, N. S. K. and Rao, P. V., “Selection of Optimum Tool Geometry and Cutting Conditions Using a Surface Roughness Prediction Model for End Milling,” The International Journal of Advanced Manufacturing Technology, vol. 26, Nos. 11–12, pp. 1202–1210, 2005.CrossRefGoogle Scholar
  14. 14.
    Savas, V. and Ozay, C., “The Optimization of the Surface Roughness in the Process of Tangential Turn-Milling Using Genetic Algorithm,” The International Journal of Advanced Manufacturing Technology, vol. 37, Nos. 3–4, pp. 335–340, 2008.CrossRefGoogle Scholar
  15. 15.
    Abburi, N. R. and Dixit, U. S., “Multi-Objective Optimization of Multipass Turning Processes,” The International Journal of Advanced Manufacturing Technology, vol. 32, Nos. 9–10, pp. 902–910, 2007.CrossRefGoogle Scholar
  16. 16.
    Mukherjee, I. and Ray, P. K., “A Review of Optimization Techniques in Metal Cutting Processes,” Computers & Industrial Engineering, vol. 50, Nos. 1–2, pp. 15–34, 2006.CrossRefGoogle Scholar
  17. 17.
    Onwubolu, G. C., “Performance-Based Optimization of Multi-Pass Face Milling Operations Using Tribes,” International Journal of Machine Tools and Manufacture, vol. 46, Nos. 7–8, pp. 717–727, 2006.CrossRefGoogle Scholar
  18. 18.
    Raja, S. B. and Baskar, N., “Application of Particle Swarm Optimization Technique for Achieving Desired Milled Surface Roughness in Minimum Machining Time,” Expert Systems with Applications, vol. 39, no. 5, pp. 5982–5989, 2012.CrossRefGoogle Scholar
  19. 19.
    Lo, S. P., “An Adaptive-Network Based Fuzzy Interference System for Predicting of Work Piece Surface Roughness in End Milling,” Journal of Materials Processing Technology, vol. 142, no. 3, pp. 665–675, 2003.CrossRefGoogle Scholar
  20. 20.
    Zhang, J. Z., Chen, J. C., and Kirby, E. D., “Surface Roughness Optimization in an End-Milling Operation Using the Taguchi Design Method,” Journal of Materials Processing Technology, vol. 184, Nos. 1–3, pp. 233–239, 2007.CrossRefGoogle Scholar
  21. 21.
    Zain, A. M., Haron, H., and Sharif, S., “Prediction of Surface Roughness in the End Milling Machining Using Artificial Neural Network,” Expert Systems with Applications, vol. 37, no. 2, pp. 1755–1768, 2010.CrossRefGoogle Scholar
  22. 22.
    Asiltürk, I. and Çunkaş, M., “Modeling and Prediction of Surface Roughness in Turning Operations Using Artificial Neural Network and Multiple Regression Method,” Expert Systems with Applications, vol. 38, no. 5, pp. 5826–5832, 2011.CrossRefGoogle Scholar
  23. 23.
    Benardos, P. G. and Vosniakos, G. C., “Prediction of Surface Roughness in CNC Face Milling Using Neural Networks and Taguchi's Design of Experiments,” Robotics and Computer-Integrated Manufacturing, vol. 18, Nos. 5–6, pp. 343–354, 2002.CrossRefGoogle Scholar
  24. 24.
    Malghan, R. L., Rao, K. M., Shettigar, A. K., Rao, S. S., and D’Souza, R., “Application of Particle Swarm Optimization and Response Surface Methodology for Machining Parameters Optimization of Aluminium Matrix Composites in Milling Operation,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 39, no. 9, pp. 3541–3553, 2017.CrossRefGoogle Scholar
  25. 25.
    Yusup, N., Zain, A. M., and Hashim, S. Z. M., “Overview of PSO for Optimizing Process Parameters of Machining,” Procedia Engineering, vol. 29, pp. 914–923, 2012.CrossRefGoogle Scholar
  26. 26.
    Zhou, J., Ren, J., Feng, Y., Tian, W., and Shi, K., “A Modified Parallel-Sided Shear Zone Model for Determining Material Constitutive Law,” The International Journal of Advanced Manufacturing Technology, vol. 91, Nos. 1–4, pp. 589–603, 2017.CrossRefGoogle Scholar
  27. 27.
    Rashid, M. F. F., Hutabarat, W., and Tiwari, A., “A Review on Assembly Sequence Planning and Assembly Line Balancing Optimisation Using Soft Computing Approaches,” The International Journal of Advanced Manufacturing Technology, vol. 59, Nos. 1–4, pp. 335–349, 2012.CrossRefGoogle Scholar
  28. 28.
    Yu, C., Xu, T., and Liu, C., “Design of a Novel UWB Omnidirectional Antenna Using Particle Swarm Optimization,” International Journal of Antennas and Propagation, Vol. 2015, Article ID: 303195, 2015.Google Scholar
  29. 29.
    Chandrasekaran, M., Muralidhar, M., Krishna, C. M., and Dixit, U., “Application of Soft Computing Techniques in Machining Performance Prediction and Optimization: A Literature Review,” The International Journal of Advanced Manufacturing Technology, vol. 46, Nos. 5–8, pp. 445–464, 2010.CrossRefGoogle Scholar
  30. 30.
    Raja, S. B. and Baskar, N., “Application of Particle Swarm Optimization Technique for Achieving Desired Milled Surface Roughness in Minimum Machining Time,” Expert Systems with Applications, vol. 39, no. 5, pp. 5982–5989, 2012.CrossRefGoogle Scholar
  31. 31.
    Rao, R. V. and Pawar, P. J., “Parameter Optimization of a Multi-Pass Milling Process Using Non-Traditional Optimization Algorithms,” Applied Soft Computing, vol. 10, no. 2, pp. 445–456, 2010.CrossRefGoogle Scholar
  32. 32.
    Yang, W.-A., Guo, Y., and Liao, W., “Multi-Objective Optimization of Multi-Pass Face Milling Using Particle Swarm Intelligence,” The International Journal of Advanced Manufacturing Technology, vol. 56, Nos. 5–8, pp. 429–443, 2011.CrossRefGoogle Scholar
  33. 33.
    Farahnakian, M., Razfar, M. R., Moghri, M., and Asadnia, M., “The Selection of Milling Parameters by the PSO-Based Neural Network Modeling Method,” The International Journal of Advanced Manufacturing Technology, vol. 57, Nos. 1–4, pp. 49–60, 2011.CrossRefGoogle Scholar
  34. 34.
    Yang, W.-A., Guo, Y., and Liao, W.-H., “Optimization of Multi-Pass Face Milling Using a Fuzzy Particle Swarm Optimization Algorithm,” The International Journal of Advanced Manufacturing Technology, vol. 54, Nos. 1–4, pp. 45–57, 2011.CrossRefGoogle Scholar
  35. 35.
    Razfar, M., Asadnia, M., Haghshenas, M., and Farahnakian, M., “Optimum Surface Roughness Prediction in Face Milling X20Cr13 Using Particle Swarm Optimization Algorithm,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 224, no. 11, pp. 1645–1653, 2010.CrossRefGoogle Scholar
  36. 36.
    Zheng, L. Y. and Ponnambalam, S., “Optimization of Multipass Turning Operations Using Particle Swarm Optimization,” Proc. of 7th International Symposium on Mechatronics and its Applications (ISMA), pp. 1–6, 2010.Google Scholar
  37. 37.
    Raja, S. B. and Baskar, N., “Optimization Techniques for Machining Operations: A Retrospective Research Based on Various Mathematical Models,” The International Journal of Advanced Manufacturing Technology, vol. 48, Nos. 9–12, pp. 1075–1090, 2010.CrossRefGoogle Scholar
  38. 38.
    Montgomery, D. C., “Design and Analysis of Experiments,” John Wiley & Sons, 2017.Google Scholar
  39. 39.
    Phadke, M. S., “Quality Engineering Using Robust Design,” Prentice Hall PTR, 1995.Google Scholar
  40. 40.
    Bement, T. R., “Taguchi Techniques for Quality Engineering,” Taylor & Francis, 1989.Google Scholar
  41. 41.
    Rai, R., Kumar, A., Rao, S., and Shriram, S., “Development of a Surface Roughness Prediction System for Machining of Hot Chromium Steel (AISI H11) Based on Artificial Neural Network,” ARPN Journal of Engineering and Applied Sciences, vol. 5, no. 11, pp. 53–59, 2010.Google Scholar
  42. 42.
    Reddy, N. S. K., Shin, K.-S., and Yang, M., “Experimental Study of Surface Integrity during End Milling of Al/SiC Particulate Metal-Matrix Composites,” Journal of Materials Processing Technology, vol. 201, Nos. 1–3, pp. 574–579, 2008.CrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNITKSurathkalIndia
  2. 2.Department of Mechanical EngineeringDebre Markos UniversityDebre MarkosEthiopia
  3. 3.Department of Mechatronics Engineering, Manipal Institute of TechnologyManipal Academy of Higher EducationManipalIndia

Personalised recommendations