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

doi:10.1007/s40436-020-00326-x

摘要/Abstract

摘要： 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00326-x

关键词: Al/SiC metal matrix composite (MMC), Surface roughness, Prediction, Control, Neural network

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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00326-x

Key words: Al/SiC metal matrix composite (MMC), Surface roughness, Prediction, Control, Neural network

Guo Zhou, Chao Xu, Yuan Ma, Xiao-Hao Wang, Ping-Fa Feng, Min Zhang. Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks[J]. Advances in Manufacturing, 2020, 8(4): 486-507.

参考文献

1. Hekner B, Myalski J, Pawlik T et al (2017) Effect of carbon in fabrication Al-SiC nanocomposites for tribological application. Materials 10(6):679. https://doi.org/10.3390/ma10060679
2. Dong Z, Zheng F, Zhu X et al (2017) Characterization of material removal in ultrasonically assisted grinding of SiCp/Al with high volume fraction. Int J Adv Manuf Technol 93(5/8):2827-2839
3. Xiang J, Xie L, Gao F et al (2018) Methodology for dependencebased integrated constitutive modelling:an illustrative application to SiCp/Al composites. Ceram Int 44(10):11765-11777
4. Ozben T, Kilickap E, Cakir O (2008) Investigation of mechanical and machinability properties of SiC particle reinforced Al-MMC. J Mater Process Technol 198(1/3):220-225
5. Kennedy FE, Balbahadur AC, Lashmore DS (1997) The friction and wear of Cu-based silicon carbide particulate metal matrix composites for brake applications. Wear 203:715-721
6. Ravikiran A, Surappa MK (1997) Effect of sliding speed on wear behaviour of A356 Al-30 wt.%SiCp MMC. Wear 206(1/2):33-38
7. Chen J, Gu L, Liu X et al (2018) Combined machining of SiC/Al composites based on blasting erosion arc machining and CNC milling. Int J Adv Manuf Technol 96(1/4):111-121
8. Monaghan JM (1996) 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 18(3):213-217
9. Hocheng H, Yen SB, Ishihara T et al (1997) Fundamental turning characteristics of a tribology-favored graphite/aluminum alloy composite material. Compos A Appl Sci Manuf 28(9/10):883-890
10. Chan KC, Cheung CF, Ramesh MV et al (2001) A theoretical and experimental investigation of surface generation in diamond turning of an Al6061/SiCp metal matrix composite. Int J Mech Sci 43(9):2047-2068
11. Pramanik A, Zhang LC, Arsecularatne JA (2008) Machining of metal matrix composites:effect of ceramic particles on residual stress, surface roughness and chip formation. Int J Mach Tools Manuf 48(15):1613-1625
12. Manna A, Bhattacharyya B (2004) Investigation for optimal parametric combination for achieving better surface finish during turning of Al/SiC-MMC. Int J Adv Manuf Technol 23(9/10):658-665
13. Palanikumar K, Karthikeyan R (2007) Assessment of factors influencing surface roughness on the machining of Al/SiC particulate composites. Mater Des 28(5):1584-1591
14. Przestacki D, Szymanski P, Wojciechowski S (2016) Formation of surface layer in metal matrix composite A359/20SiCP during laser assisted turning. Compos A Appl Sci Manuf 91:370-379
15. Wojciechowski S, Nowakowski Z, Majchrowski R et al (2017) Surface texture formation in precision machining of direct laser deposited tungsten carbide. Adv Manuf 5(3):251-260
16. Kilickap E (2016) Effect of cutting environment and heat treatment on the surface roughness of drilled Al/SiC MMC. Mater Test 58(4):357-361
17. Kilickap E, Cakir O, Aksoy M et al (2005) Study of tool wear and surface roughness in machining of homogenized SiC-p reinforced aluminium metal matrix composite. J Mater Process Technol 164/165:862-867
18. Benardros PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments. Robot Comput Integrated Manuf 18(5/6):343-354
19. Mahesh G, Muthu S, Devadasan SR (2015) Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol 77(1/4):369-381
20. Kilickap E, Huseyinoglu M, Yardimeden (2011) 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 52(1/4):79-88
21. Khorasani AM, Yazdi MRS, Safizadeh MS (2011) 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 3(1):30-35
22. Pimenov DY, Hassui A, Wojciechowski S et al (2019) Effect of the relative position of the face milling tool towards the workpiece on machined surface roughness and milling dynamics. Appl Sci 9(5):842. https://doi.org/10.3390/app9050842
23. Bustillo A, Correa M (2012) Using artificial intelligence to predict surface roughness in deep drilling of steel components. J Intell Manuf 23(5):1893-1902
24. Rodríguez JJ, Quintana G, Bustillo A et al (2017) A decisionmaking tool based on decision trees for roughness prediction in face milling. Int J Comput Integr Manuf 30(9):943-957
25. Çelik YH, Kilickap E, Yardimeden A (2014) Estimate of cutting forces and surface roughness in end milling of glass fiber reinforced plastic composites using fuzzy logic system. Sci Eng Compos Mater 21(3):435-443
26. Lin JT, Bhattacharyya D, Kecman V (2003) Multiple regression and neural networks analyses in composites machining. Compos Sci Technol 63(3/4):539-548
27. Mia M, Królczyk G, Maruda R et al (2019) Intelligent optimization of hard-turning parameters using evolutionary algorithms for smart manufacturing. Materials 12(6):879. https://doi.org/10.3390/ma12060879
28. Bustillo A, Díez-Pastor JF, Quintana G et al (2011) Avoiding neural network fine tuning by using ensemble learning:application to ball-end milling operations. Int J Adv Manuf Technol 57(5/8):521. https://doi.org/10.1007/s00170-011-3300-z
29. Kilickap E, Yardimeden A, Çelik YH (2017) Effect of cutting environment and heat treatment on the surface roughness in milling of Ti-6242S. Appl Sci 7(10):1064. https://doi.org/10.3390/app7101064
30. Manna A, Bhattacharayya B (2003) A study on machinability of Al/SiC-MMC. J Mater Process Technol 140(1/3):711-716
31. Xiang JF, Pang SQ, Xie LJ et al (2018) 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 98:1237-1251
32. Yuan ZJ, Geng L, Dong S (1993) Ultraprecision machining of SiCw/Al composites. CIRP Ann 42(1):107-109
33. Sahoo AK, Pradhan S (2013) Modeling and optimization of Al/SiCp MMC machining using Taguchi approach. Measurement 46(9):3064-3072
34. Khorasani AM, Yazdi MRS, Safizadeh MS (2011) 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 3(1):30-35
35. Gologlu C, Sakarya N (2008) The effects of cutter path strategies on surface roughness of pocket milling of 1.2738 steel based on Taguchi method. J Mater Process Technol 206(1/3):7-15
36. YalcinU Karaoglan AD, Korkut I (2013) Optimization of cutting parameters in face milling with neural networks and Taguchi based on cutting force, surface roughness and temperatures. Int J Prod Res 51(11):3404-3414
37. El-Gallab M, Sklad M (1998) Machining of Al/SiC particulate metal-matrix composites:part I:tool performance. J Mater Process Technol 83(1/3):151-158
38. Grzenda M, Bustillo A, Zawistowski P (2012) A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. J Intell Manuf 23(5):1733-1743
39. Pala M, Caglar N, Elmas M et al (2008) Dynamic soil-structure interaction analysis of buildings by neural networks. Constr Build Mater 22(3):330-342
40. Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Pub. Co., Boston, p 3632
41. Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989-993
42. MacKay David JC (1991) Bayesian interpolation. Neural Comput 4(3):415-447
43. Bustillo A, Grzenda M, Macukow B (2016) Interpreting treebased prediction models and their data in machining processes. Integr Comput Aided Eng 23(4):349-367
44. Karabulut Ş (2015) Optimization of surface roughness and cutting force during AA7039/Al₂O₃ metal matrix composites milling using neural networks and Taguchi method. Measurement 66:139-149

[1]	Peng Lyu, Min Lai, Feng-Zhou Fang. Nanometric polishing of lutetium oxide by plasma-assisted etching[J]. Advances in Manufacturing, 2020, 8(4): 440-446.
[2]	Wei Han, Feng-Zhou Fang. Investigation of electropolishing characteristics of tungsten in ecofriendly sodium hydroxide aqueous solution[J]. Advances in Manufacturing, 2020, 8(3): 265-278.
[3]	Mirsadegh Seyedzavvar, Hossein Abbasi, Mehdi Kiyasatfar, Reza Najati Ilkhchi. Investigation on tribological performance of CuO vegetable-oil based nanofluids for grinding operations[J]. Advances in Manufacturing, 2020, 8(3): 344-360.
[4]	Wei Zhao, Asif Iqbal, Ding Fang, Ning He, Qi Yang. Experimental study on the meso-scale milling of tungsten carbide WC-17.5Co with PCD end mills[J]. Advances in Manufacturing, 2020, 8(2): 230-241.
[5]	Annamaria Gisario, Mehrshad Mehrpouya, Atabak Rahimzadeh, Andrea De Bartolomeis, Massimiliano Barletta. Prediction model for determining the optimum operational parameters in laser forming of fiber-reinforced composites[J]. Advances in Manufacturing, 2020, 8(2): 242-251.
[6]	Zhi Chen, Jian Fu, Xiao-Wei Tu, Ao-Lei Yang, Min-Rui Fei. Real-time predictive sliding mode control method for AGV with actuator delay[J]. Advances in Manufacturing, 2019, 7(4): 448-459.
[7]	Xiang Zou, Hon-Yuen Tam, Hai-Yin Xu, Ke Shi. Flat-end tool orientation based on rotation-minimizing frame[J]. Advances in Manufacturing, 2019, 7(3): 257-269.
[8]	Ruo-Yu Yang, Rahul Rai. Machine auscultation: enabling machine diagnostics using convolutional neural networks and large-scale machine audio data[J]. Advances in Manufacturing, 2019, 7(2): 174-187.
[9]	Mohsen Poorzeinolabedin, Kemal Levend Parnas. Flow correction control with electromagnetically induced preform resting process[J]. Advances in Manufacturing, 2019, 7(2): 199-208.
[10]	Shuntaro Yamato, Yuki Yamada, Kenichi Nakanishi, Norikazu Suzuki, Hayato Yoshioka, Yasuhiro Kakinuma. Integrated in-process chatter monitoring and automatic suppression with adaptive pitch control in parallel turning[J]. Advances in Manufacturing, 2018, 6(3): 291-300.
[11]	Li-Yao Gu, Min-Jie Wang. Adiabatic shear fracture prediction in high-speed cutting at various negative rake angles and feeds[J]. Advances in Manufacturing, 2018, 6(1): 41-51.
[12]	Ramanuj Kumar, Ashok Kumar Sahoo, Purna Chandra Mishra, Rabin Kumar Das. Comparative investigation towards machinability improvement in hard turning using coated and uncoated carbide inserts: part I experimental investigation[J]. Advances in Manufacturing, 2018, 6(1): 52-70.
[13]	Jo Wessel Strandhagen, Erlend Alfnes, Jan Ola Strandhagen, Logan Reed Vallandingham. The fit of Industry 4.0 applications in manufacturing logistics: a multiple case study[J]. Advances in Manufacturing, 2017, 5(4): 344-358.
[14]	Nikita Voinov, Igor Chernorutsky, Pavel Drobintsev, Vsevolod Kotlyarov. An approach to net-centric control automation of technological processes within industrial IoT systems[J]. Advances in Manufacturing, 2017, 5(4): 388-393.
[15]	Saroj Kumar Padhi, Ranjeet Kumar Sahu, S. S. Mahapatra, Harish Chandra Das, Anoop Kumar Sood, Brundaban Patro, A. K. Mondal. Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy[J]. Advances in Manufacturing, 2017, 5(3): 231-242.