Fuzzy logic-integrated PSO methodology for parameters optimization in end milling of Al/SiCp MMC
- 13 Downloads
This paper deals with a novel integrated parametric optimization in end milling of Al–SiCp metal–matrix composites using carbide tool. Fuzzy logic modeling combined with particle swarm optimization (PSO) technique is applied as intelligent optimization methodology to obtain optimal process parameters in end milling process. Fuzzy rules-based surface roughness prediction model is developed with SiCp percentage as one the input parameter is addition to spindle speed, feed and depth of cut. Comparison of fuzzy rule model predictions with confirmation data sets showed 95.44% average prediction accuracy. The effect of process parameters on surface roughness is also studied. Analysis of variance established feed rate as the most significant process variable with 69.93% contribution followed by SiCp (19%) and spindle speed (9.73%), respectively. The integrated PSO optimizer optimizes the process parameters for an objective to minimize the machining time satisfying desired surface roughness to be obtained. A number of problems with different values of desired surface roughness are solved and algorithm converged in less than ten iterations.
KeywordsPSO Surface roughness Metal–matrix composites Fuzzy logic Optimization
Authors acknowledge the sincere effort of anonymous reviewers for their constructive comments and valuable suggestions in improving the manuscript in the present form. Special thanks to the Editorial team of this journal for their kind support extended throughout the publication process.
- 18.Dikshit MK, Asit BP, Maity A (2017) Modelling and application of response surface optimization to optimize cutting parameters for minimizing cutting forces and surface roughness in high-speed, ball-end milling of Al2014-T6. J Braz Soc Mech Sci Eng. https://doi.org/10.1007/s40430-017-0865-y CrossRefGoogle Scholar
- 21.Rahul K, Abhishek Saurav D, Bibhuti BB, Mahapatra SS (2016) Machining performance optimization for electro-discharge machining of Inconel 601, 625, 718 and 825: an integrated optimization route combining satisfaction function, fuzzy inference system and Taguchi approach. J Braz Soc Mech Sci Eng. https://doi.org/10.1007/s40430-016-0659-7 CrossRefGoogle Scholar
- 24.Arokiadass R, Palanirajda K, Alagumoorthi N (2011) Predictive modeling of surface roughness in end milling of Al/SiCp metal matrix composite. Arch Appl Sci Res 3(2):228–236Google Scholar
- 26.Dixit PM, Dixit US (2008) Modelling of metal forming and machining processes by finite element and soft computing methods. Springer, London, pp 490–491Google Scholar
- 27.Chandrasekaran M, Muralidhar M, Krishna CM, Dixit US (2012) Online machining optimization with continuous learning. In: Paulo Davim J (ed) Computational methods for optimizing manufacturing technology: models and techniques. Engineering science reference. IGI Global Publication, Hershey, PA, pp 85–110CrossRefGoogle Scholar
- 28.Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, University of Western Australia, Perth, Western Australia, pp 1942–1948Google Scholar