Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

Abstract

During the actual high-speed machining process, it is necessary to reduce the energy consumption and improve the machined surface quality. However, the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining environments. Herein, a novel intelligent system is proposed for prediction and optimization. A novel adaptive neuro-fuzzy inference system (NANFIS) is proposed to predict the energy consumption and surface quality. In the NANFIS model, the membership functions of the inputs are expanded into: membership superior and membership inferior. The membership functions are varied based on the machining theory. The inputs of the NANFIS model are cutting parameters, and the outputs are the machining performances. For optimization, the optimal cutting parameters are obtained using the improved particle swarm optimization (IPSO) algorithm and NANFIS models. Additionally, the IPSO algorithm as a learning algorithm is used to train the NANFIS models. The proposed intelligent system is applied to the high-speed milling process of compacted graphite iron. The experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2% and 93.4%, respectively. The NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent models. Based on the IPSO algorithm and NANFIS models, the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling efficiency. It is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes, thereby enabling sustainable and intelligent manufacturing.

Keywords

Novel adaptive neuro-fuzzy inference system (NANFIS) model / Improved particle swarm optimization (IPSO) algorithm / Energy consumption / Surface roughness / Multiobjective optimization

References

[1] Zhong N, Zhou YX, Zhu XF, et al. Microstructure and cutting performance of carbonitride coated tools in high speed machining of 40Cr steel. Surf Eng, 2011, 27(4): 306-310.

[2] Tlhabadira I, Daniyan IA, Machaka R, et al. Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method. Int J Adv Manuf Tech, 2019, 102(9): 3707-3718.

[3] Shi KN, Ren JX, Wang SB, et al. An improved cutting power-based model for evaluating total energy consumption in general end milling process. J Clean Prod, 2019, 231: 1330-1341.

[4] Zhu CM, Gu P, Wu YY, et al. Surface roughness prediction model of SiCp/Al composite in grinding. Int J Mech Sci, 2019, 155: 98-109.

[5] Liu N, Zhang YF, Lu WF. A hybrid approach to energy consumption modelling based on cutting power: a milling case. J Clean Prod, 2015, 104: 264-272.

[6] He Y, Wang LX, Wang YL, et al. An analytical model for predicting specific cutting energy in whirling milling process. J Clean Prod, 2019, 240: 1-16.

[7] Kant G, Sangwan KS. Predictive modeling for energy consumption in machining using artificial neural network. Proc CIRP, 2015, 37: 205-210.

[8] Pimenov DY, Bustillo A, Mikolajczyk T. Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. J Intell Manuf, 2018, 29(5): 1045-1061.

[9] Bustillo A, Pimenov DY, Mia M, et al. Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. J Intell Manuf, 2020.

[10] Lau HCW, Cheng ENM, Lee CKM, et al. A fuzzy logic approach to forecast energy consumption change in a manufacturing system. Expert Syst Appl, 2008, 34(3): 1813-1824.

[11] Ullah I, Ahmad R, Kim D. A prediction mechanism of energy consumption in residential buildings using hidden Markov model. Energies, 2018, 11(2): 358-359.

[12] Misaka T, Herwan J, Ryabov O, et al. Prediction of surface roughness in CNC turning by model-assisted response surface method. Precis Eng, 2020, 62: 196-203.

[13] Li LB, Wu MY, Liu XL, et al. The prediction of surface roughness of PCBN turning GH4169 based on adaptive genetic algorithm. Integr Ferroelectr, 2017, 180(1): 118-132.

[14] Rizal M, Ghani JA, Nuawi MZ, et al. Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system. Appl Soft Comput, 2013, 13: 1960-1968.

[15] Maher I, Eltaib MEH, Sarhan A, et al. Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling-ANFIS modeling. Int J Adv Manuf Tech, 2014, 74(1): 531-537.

[16] Sparham M, Sarhan AAD, Mardi NA, et al. ANFIS modeling to predict the friction forces in CNC guideways and servomotor currents in the feed drive system to be employed in lubrication control system. J Manuf Process, 2017, 28: 168-185.

[17] Sarkheyli A, Azlan MZ, Safian S. Robust optimization of ANFIS based on a new modified GA. Neurocomputing, 2015, 166: 357-366.

[18] Adedeji PA, Stephen A, Nkosinathi M, et al. Wind turbine power output very short-term forecast: a comparative study of data clustering techniques in a PSO-ANFIS model. J Clean Prod, 2020, 254: 120-135.

[19] Hasanipanah M, Amnieh HB, Arab H, et al. Feasibility of PSO-ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl, 2018, 30: 1015-1024.

[20] Pimenov DY, Abbas AT, Gupta MK, et al. Investigations of surface quality and energy consumption associated with costs and material removal rate during face milling of AISI 1045 steel. Int J Adv Manuf Tech, 2020, 107(7): 3511-3525.

[21] Abbas AT, Pimenov DY, Erdakov IN, et al. Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel. Int J Adv Manuf Tech, 2019, 105(5): 2151-2165.

[22] Krimpenis AA, Fountas NA. Balancing multiple criteria in formulation of weighted, single-objective genetic algorithm optimization for CNC machining problems. Adv Manuf, 2016, 4: 178-188.

[23] Manivel D, Gandhinathan R. Optimization of surface roughness and tool wear in hard turning of austempered ductile iron (grade 3) using Taguchi method. Measurement, 2016, 93: 108-116.

[24] Negrete C. Optimization of cutting parameters using response surface method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. J Clean Prod, 2015, 91: 109-117.

[25] Keshtiara M, Golabi S, Tarkesh ER. Multi-objective optimization of stainless steel 304 tube laser forming process using GA. Eng Comput-Germany, 2019.

[26] Babaei M, Mollayi M. Multi-objective optimization of reinforced concrete frames using NSGA-II algorithm. Eng Struct Technol, 2016, 8(4): 157-164.

[27] Li L, Deng X, Zhao J, et al. Multi-objective optimization of tool path considering efficiency, energy-saving and carbon-emission for free-form surface milling. J Clean Prod, 2018, 172: 3311-3322.

[28] Ampellio E, Vassio L. A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses. Swarm Intell-US, 2016, 10(2): 99-121.

[29] Saw LH, Ho LW, Yew MC, et al. Sensitivity analysis of drill wear and optimization using adaptive neuro fuzzy-genetic algorithm technique toward sustainable machining. J Clean Prod, 2018, 172: 3289-3298.

[30] Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern, 1993, 23: 665-685.

[31] Hosoz M, Ertunc HM, Bulgurcu H. An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower. Expt Syst Appl, 2011, 38: 14148-14155.

[32] Dewan MW, Huggett DJ, Liao TW, et al. Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network. Mater Design, 2016, 92: 288-299.

[33] Kennedy J, Eberhart RC, Shi Y. Swarm Intelligence, 2001, San Francisco: Morgan Kaufmann Publishers

[34] Hoang TT, Cho MY, Alam MN, et al. A novel differential particle swarm optimization for parameter selection of support vector machines for monitoring metal-oxide surge arrester conditions. Swarm Evol Comput, 2018, 38: 120-126.

[35] Xu L, Huang C, Li C, et al. An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining. J Intell Manuf, 2020.

[36] Li JG, Lu Y, Zhao H, et al. Optimization of cutting parameters for energy saving. Int J Adv Manuf Tech, 2014, 70: 117-124.

[37] Kant G, Sangwan K. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. J Clean Prod, 2014, 83: 151-164.

Funding

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(51675312)