Multi-objective Optimization of Surface Roughness and Material Removal Rate Using an Improved Self-Adaptive Particle Swarm Optimization Algorithm in Milling process

Document Type : Research Article

Author

Faculty of Mechanical Engineering, Yazd University, P.O.B. 89195-741, Yazd, Iran

Abstract

Surface roughness is one of the main characteristics of a work piece in the quality control process. Several parameters such as cutting tool material and geometry, cutting parameters, work piece material properties, machine tool and coolant type affect the surface quality. An important task of process planners is the proper selection of three main cutting parameters: cutting speed, feed rate, and depth of cut in order to have not only low surface roughness, but also to perform the process within a reasonable amount of time. In this paper, using full factorial experiment design, the multiple regression equation for the surface roughness in the climb milling process of DIN 1.4021 martensitic stainless steel has been obtained and then used as one of the objective functions in the Multi-objective Improved Self- Adaptive Particle Swarm Optimization (MISAPSO) algorithm. This algorithm has been used to obtain cutting parameters to achieve low surface roughness simultaneously with a high material removal rate. The relatively new algorithm MISAPSO developed with some changes in the common particle swarm optimization (PSO) technique, has been used in multi-objective optimization of machining processes and was shown to be able to help the process planners in selecting cutting parameters.

Keywords


[1] P.G. Benardos, G.C. Vosniakos, Predicting Surface Roughness in Machining: A Review, Int J Mach Tool Manuf, 43 (2003) 833–844.
[2] M. Chandrasekaran, M. Muralidhar, C. Murali Krishna, U.S. Dixit, Application of soft computing techniques in machining performance prediction and optimization: a literature review, Int J Adv Manuf Technol, 46 (2010) 445-464.
[3] C.X. Feng, X. Wang, Development of empirical models for surface roughness prediction in finish turning, Int J Mach Tool Manuf, 20 (2002) 348–356.
[4] B. Ozcelik, M. Bayramoglu, The statistical modeling of surface roughness in high-speed flat end milling, Int J Mach Tool Manuf, 46 (2006) 1395–1402.
[5] H. Aouici, M.A. Yallese, B. Fnides, K. Chaoui, T. Mabrouki, Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: machining parameters effects on flank wear and surface roughness, J Mech Sci Technol, 25 (2011) 2843–2851.
[6] S. Bharathi Raja, N. Baskar, Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time, Expert Syst Appl, 39 (2012) 5982–5989.
[7] T. Kivak, Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts, Measurement, 50 (2014) 19–28.
[8] G.M.A. Acayaba, P.M. Escalona, Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel, CIRP J Manuf Sci Technol, 11 (2015) 62–67.
[9] M. Hanief, M.F. Wani, Modeling and prediction of surface roughness for running-in wear using Gauss-Newton algorithm and ANN, Appl Surf Sci, 357 (2015) 1573–1577.
[10] A. Gok, A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA, Measurement, 70 (2015) 100–109.
[11] M. Gupta, S. Kumar, Investigation of surface roughness and MRR for turning of UD-GFRP using PCA and Taguchi method, Int J Eng Sci Technol, 18 (2015) 70-81.
[12] A. Nejat, H.R. Kaviani, Aerodynamic optimization of a megawatt class horizontal axis wind turbine blade with particle swarm optimization algorithm, Modares Mechanical Engineering, 16(11) (2016) 1-11. (in Persian)
[13] M. Fallah, B. Moetakef Imani, Updating boring bar’s dynamic model using particle swarm optimization, Modares Mechanical Engineering, 16(12) (2016) 479-489. (in Persian)
[14] The Atlas Specialty Metals-Technical Handbook of Stainless Steels, (2016). http://www.atlasmetals.com.au
[15] ISO 4287, Geometrical Product Specifications (GPS) Surface Texture: Profile Method Terms, Definitions and Surface Texture Parameters. International Organization for Standardization, Geneva, 1997.
[16] E. Budak, A. Takeli, Maximizing Chatter Free Material Removal Rate in Milling through Optimal Selection of Axial and Radial Depth of Cut Pairs, CIRP Ann Manu Techn, 54 (2005) 353–356.
[17] M. Moradi, et al., Parameter dependencies in laser hybrid arc welding by design of experiments and by a mass balance, Journal of Laser Applications, 26 (2014) 1-9.
[18] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition, Springer-Verlag, New York, 2009.
[19] H. Zeinoddini-Meymand, B. Vahidi, R.A. Naghizadeh, M. Moghimi, Optimal Surge Arrester Parameter Estimation Using a PSO-Based Multiobjective Approach, IEEE Trans Power Delivery, 28 (2013) 1758-1769.
[20] R. Caponetto, L. Fortuna, S. Fazzino, M.G. Xibilia, Chaotic sequences to improve the performance of evolutionary algorithms, IEEE Trans Evol Comput, 7 (2003) 289-304.
[21] C.M. Lin, M. Gen, Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multi-objective hybrid genetic algorithm, Expert Syst Appl, 34 (2008) 2480-2490.
[22] N.T. Thomopoulos, Essentials of Monte Carlo Simulation: Statistical Methods for Building Simulation Models, Springer-Verlag, New York, 2013.