Performance Enhancement of CNC Milling Process using Different Machine Learning Techniques
DOI:
https://doi.org/10.18311/jmmf/2023/33377Keywords:
ANN, KNN, Linear RegressionAbstract
Towards the requirement of technological development, it is necessary to optimize the machining parameters in the manufacturing process. Nowadays machine learning algorithms have proven their potential towards a performance enhancement process compared to conventional methods. In the present study, the different machine learning methods such as Linear Regression, Decision tree algorithm, ANN are used for prediction of process parameters of CNC milling process. Performance Enhancement can be achieved through predicting the proper combination of cutting speed, feed and depth of cut with the objectives of Minimum machining time, maximum material removal rate, minimum surface roughness and maximum tool life. Mathematical models are developed using a regression tool in MINITAB. Modelling of machine learning algorithms is done using python codes. Comparison of different machine learning algorithms are done to select the best optimization tool for this process enhancement procedure.
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References
S. Ajith Arul Daniel, R. Pugazhenthi, R. Kumar, S. Vijayananth, (2019): “Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-Grey relational analysis”, Defense Technology 15 (2019) 545-556.
Xuefeng Wu, Xuefeng Yin, (2018): “Surface roughness analysis and parameter optimization of mold steel milling”, Procedia CIRP 71 (2018) 317–321
Muhammed Muaz, Sounak Kumar Choudhury, (2019): “Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel”, Measurement 138 (2019) 557–569
Kanchana J, prasath V, Krishnaraj V, Geetha Priyadharshini, (2019): “Multi response optimization of process parameters using Grey relational analysis for milling of hardened custom 465 steel”, Procedia Manufacturing 30 (2019) 451–458.
Laura Peña-Parása, Demófilo Maldonado-Cortésa, Martha Rodríguez-Villalobosb, Angel G. Romero- Cantúa, Oscar E. Montemayora, Mónica Herreraa, Gabriela Troussellea, Jaime Gonzáleza, Walter Huglerc, (2019): “Optimization of milling parameters of 1018 steel and nanoparticle additive concentration in cutting fluids for enhancing multi-response characteristics”, Wear 426–427 (2019) 877–886
Kasper Ringgaarda, Yaser Mohammadib, Christian Merrildc, Ole Ballinga, Keivan Ahmadib, (2019): “Optimization of material removal rate in milling of thin-walled structures using penalty cost function”, International Journal of Machine Tools and Manufacture 145 (2019) 103430
I.A. Daniyana, I. Tlhabadirab, O.O. Daramolac, S.N. Phokobyed, M. Siviwed, K. Mpofua, (2020): “Measurement and optimization of cutting forces during M200 TS milling process using the response surface methodology and dynamometer, Procedia CIRP 88 (2020) 288–293
Lucas Guedes de Oliveira, Carlos Henrique de Oliveira, Tarcísio Gonçalves de Brito, Emerson Jos´e de Paiva, Anderson Paulo de Paiva, Joao Roberto Ferreira, (2020): “Nonlinear optimization strategy based on multivariate prediction capability ratios: Analytical schemes and model validation for duplex stainless steel end milling”, Precision Engineering 66 (2020) 229–254
B Schmucker, F Trautwein, R Hartl, A Lechler, M F Zaeh, A Veerl, (2022): “Online Parameterization of a Milling Force Model using an Intelligent System Architecture and bayesian Optimization”, Procedia CIRP 107 (2022) 1041–1046
Al Mazedur Rahmana, S M Abdur Roba, Anil K. Srivastavaa, (2021): “Modelling and optimization of process parameters in face milling of Ti6Al4V alloy using Taguchi and grey relational analysis”, Procedia Manufacturing 53 (2021) 204–212
Lei Liu, Da Qu c, Huajun Cao, Xuefeng Huang, Yang Song, Xinzhen Kang, (2022): “Process optimization of high machining efficiency and low surface defects for HSD milling UD-CF/PEEK with limited thermal effect”, Journal of Manufacturing Processes 76 (2022) 532– 547
Pengfei Ding, Xianzhen Huang, Xuewei Zhang, Yuxiong Li, Changli Wang, (2022): “Reliability optimization of micro-milling cutting parameters using slime mould sequence algorithm”, Simulation Modelling Practice and Theory 119 (2022) 102575
M Shivasheshadri, M Arunadevi, P CPS Prakash, (2012): “Simulation approach and optimization of machining parameters in CNC milling machine using genetic algorithm”, International Journal of Engineering Research & Technology (IJERT) (Vol.1 Issue/ 10, ISSN: 2278-0181 ).
Varsharani Gaike, Jambeswar sahu, Raju pawade, (2018): “Optimization of cutting parameters for cutting ForceMinimization in Helicall Ball end milling of Inconel 718 by using genetic algorithm”,Procedia CIRP 77 (2018) 477–480
S.L. Campanelli, G. Casalino, N. Contuzzi, (2013): “Multi-objective optimization of laser milling of 5754 aluminum alloy”, Optics & Laser Technology 52(2013) 48–56