Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

Abstract

In recent years, there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields, and the demand for accurate machining of such composite materials has grown accordingly. In this paper, a feed-forward multi-layered artificial neural network (ANN) roughness prediction model, using the Levenberg-Marquardt backpropagation training algorithm, is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials. Milling experiments were conducted on a computer numerical control (CNC) milling machine with polycrystalline diamond (PCD) tools to acquire data for training the ANN roughness prediction model. Four cutting parameters were considered in these experiments: cutting speed, depth of cut, feed rate, and volume fraction of SiC. These parameters were also used as inputs for the ANN roughness prediction model. The output of the model was the average surface roughness of the machined workpiece. A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters, with a 2.08% mean relative error. Moreover, a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91% mean relative error was developed based on the ANN roughness prediction model. Finally, a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.

Keywords

Al/SiC metal matrix composite (MMC) / Surface roughness / Prediction / Control / Neural network

References

[1] Hekner B, Myalski J, Pawlik T, et al. Effect of carbon in fabrication Al-SiC nanocomposites for tribological application. Materials, 2017, 10(6): 679.

[2] Dong Z, Zheng F, Zhu X, et al. Characterization of material removal in ultrasonically assisted grinding of SiCp/Al with high volume fraction. Int J Adv Manuf Technol, 2017, 93(5/8): 2827-2839.

[3] Xiang J, Xie L, Gao F, et al. Methodology for dependence-based integrated constitutive modelling: an illustrative application to SiCp/Al composites. Ceram Int, 2018, 44(10): 11765-11777.

[4] Ozben T, Kilickap E, Cakir O. Investigation of mechanical and machinability properties of SiC particle reinforced Al-MMC. J Mater Process Technol, 2008, 198(1/3): 220-225.

[5] Kennedy FE, Balbahadur AC, Lashmore DS. The friction and wear of Cu-based silicon carbide particulate metal matrix composites for brake applications. Wear, 1997, 203: 715-721.

[6] Ravikiran A, Surappa MK. Effect of sliding speed on wear behaviour of A356 Al-30 wt.%SiCp MMC. Wear, 1997, 206(1/2): 33-38.

[7] Chen J, Gu L, Liu X, et al. Combined machining of SiC/Al composites based on blasting erosion arc machining and CNC milling. Int J Adv Manuf Technol, 2018, 96(1/4): 111-121.

[8] Monaghan JM. The use of a quick-stop test to study the chip formation of a SiC/Al metal matrix composite material and its matrix alloy. Int J Fatigue, 1996, 18(3): 213-217.

[9] Hocheng H, Yen SB, Ishihara T, et al. Fundamental turning characteristics of a tribology-favored graphite/aluminum alloy composite material. Compos A Appl Sci Manuf, 1997, 28(9/10): 883-890.

[10] Chan KC, Cheung CF, Ramesh MV, et al. A theoretical and experimental investigation of surface generation in diamond turning of an Al6061/SiCp metal matrix composite. Int J Mech Sci, 2001, 43(9): 2047-2068.

[11] Pramanik A, Zhang LC, Arsecularatne JA. Machining of metal matrix composites: effect of ceramic particles on residual stress, surface roughness and chip formation. Int J Mach Tools Manuf, 2008, 48(15): 1613-1625.

[12] Manna A, Bhattacharyya B. Investigation for optimal parametric combination for achieving better surface finish during turning of Al/SiC-MMC. Int J Adv Manuf Technol, 2004, 23(9/10): 658-665.

[13] Palanikumar K, Karthikeyan R. Assessment of factors influencing surface roughness on the machining of Al/SiC particulate composites. Mater Des, 2007, 28(5): 1584-1591.

[14] Przestacki D, Szymanski P, Wojciechowski S. Formation of surface layer in metal matrix composite A359/20SiCP during laser assisted turning. Compos A Appl Sci Manuf, 2016, 91: 370-379.

[15] Wojciechowski S, Nowakowski Z, Majchrowski R, et al. Surface texture formation in precision machining of direct laser deposited tungsten carbide. Adv Manuf, 2017, 5(3): 251-260.

[16] Kilickap E. Effect of cutting environment and heat treatment on the surface roughness of drilled Al/SiC MMC. Mater Test, 2016, 58(4): 357-361.

[17] Kilickap E, Cakir O, Aksoy M, et al. Study of tool wear and surface roughness in machining of homogenized SiC-p reinforced aluminium metal matrix composite. J Mater Process Technol, 2005, 164/165: 862-867.

[18] Benardros PG, Vosniakos GC. Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput Integrated Manuf, 2002, 18(5/6): 343-354.

[19] Mahesh G, Muthu S, Devadasan SR. Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol, 2015, 77(1/4): 369-381.

[20] Kilickap E, Huseyinoglu M, Yardimeden. A optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol, 2011, 52(1/4): 79-88.

[21] Khorasani AM, Yazdi MRS, Safizadeh MS. Tool life prediction in face milling machining of 7075 Al by using artificial neural networks (ANN) and Taguchi design of experiment (DOE). Int J Eng Technol, 2011, 3(1): 30-35.

[22] Pimenov DY, Hassui A, Wojciechowski S, et al. Effect of the relative position of the face milling tool towards the workpiece on machined surface roughness and milling dynamics. Appl Sci, 2019, 9(5): 842.

[23] Bustillo A, Correa M. Using artificial intelligence to predict surface roughness in deep drilling of steel components. J Intell Manuf, 2012, 23(5): 1893-1902.

[24] Rodríguez JJ, Quintana G, Bustillo A, et al. A decision-making tool based on decision trees for roughness prediction in face milling. Int J Comput Integr Manuf, 2017, 30(9): 943-957.

[25] Çelik YH, Kilickap E, Yardimeden A. Estimate of cutting forces and surface roughness in end milling of glass fiber reinforced plastic composites using fuzzy logic system. Sci Eng Compos Mater, 2014, 21(3): 435-443.

[26] Lin JT, Bhattacharyya D, Kecman V. Multiple regression and neural networks analyses in composites machining. Compos Sci Technol, 2003, 63(3/4): 539-548.

[27] Mia M, Królczyk G, Maruda R, et al. Intelligent optimization of hard-turning parameters using evolutionary algorithms for smart manufacturing. Materials, 2019, 12(6): 879.

[28] Bustillo A, Díez-Pastor JF, Quintana G, et al. Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations. Int J Adv Manuf Technol, 2011, 57(5/8): 521.

[29] Kilickap E, Yardimeden A, Çelik YH. Effect of cutting environment and heat treatment on the surface roughness in milling of Ti-6242S. Appl Sci, 2017, 7(10): 1064.

[30] Manna A, Bhattacharayya B. A study on machinability of Al/SiC-MMC. J Mater Process Technol, 2003, 140(1/3): 711-716.

[31] Xiang JF, Pang SQ, Xie LJ, et al. Investigation of cutting forces, surface integrity, and tool wear when high-speed milling of high-volume fraction SiCp/Al6063 composites in PCD tooling. Int J Adv Manuf Technol, 2018, 98: 1237-1251.

[32] Yuan ZJ, Geng L, Dong S. Ultraprecision machining of SiCw/Al composites. CIRP Ann, 1993, 42(1): 107-109.

[33] Sahoo AK, Pradhan S. Modeling and optimization of Al/SiCp MMC machining using Taguchi approach. Measurement, 2013, 46(9): 3064-3072.

[34] Khorasani AM, Yazdi MRS, Safizadeh MS. Tool life prediction in face milling machining of 7075 Al by using artificial neural networks (ANN) and Taguchi design of experiment (DOE). Int J Eng Technol, 2011, 3(1): 30-35.

[35] Gologlu C, Sakarya N. The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. J Mater Process Technol, 2008, 206(1/3): 7-15.

[36] YalcinU Karaoglan AD, Korkut I. Optimization of cutting parameters in face milling with neural networks and Taguchi based on cutting force, surface roughness and temperatures. Int J Prod Res, 2013, 51(11): 3404-3414.

[37] El-Gallab M, Sklad M. Machining of Al/SiC particulate metal-matrix composites: part I:tool performance. J Mater Process Technol, 1998, 83(1/3): 151-158.

[38] Grzenda M, Bustillo A, Zawistowski P. A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. J Intell Manuf, 2012, 23(5): 1733-1743.

[39] Pala M, Caglar N, Elmas M, et al. Dynamic soil-structure interaction analysis of buildings by neural networks. Constr Build Mater, 2008, 22(3): 330-342.

[40] Hagan MT, Demuth HB, Beale MH. Neural network design, 1996, Boston: PWS Pub. Co. 3632

[41] Hagan MT, Menhaj MB. Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw, 1994, 5(6): 989-993.

[42] MacKay David JC. Bayesian interpolation. Neural Comput, 1991, 4(3): 415-447.

[43] Bustillo A, Grzenda M, Macukow B. Interpreting tree-based prediction models and their data in machining processes. Integr Comput Aided Eng, 2016, 23(4): 349-367.

[44] Karabulut Ş. Optimization of surface roughness and cutting force during AA7039/Al₂O₃ metal matrix composites milling using neural networks and Taguchi method. Measurement, 2015, 66: 139-149.

Funding

National High Technology Research and Development Plan of China(2015AA043505)

Equipment Advanced Research Funds(61402100401)

Equipment Advanced Research Key Laboratary Funds(6142804180106)

Shenzhen Fundamental Research Funds(JCYJ20180508151910775)