Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS
Machining process is currently widely employed in mechanical manufacturing systems. Optimum selection of machining process parameters can improve the environmental impact and production efficiency of the machining process effectively. However, existing studies toward machining process parameters opt...
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| Vydáno v: | Journal of manufacturing systems Ročník 64; s. 40 - 52 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2022
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| Témata: | |
| ISSN: | 0278-6125, 1878-6642 |
| On-line přístup: | Získat plný text |
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| Abstract | Machining process is currently widely employed in mechanical manufacturing systems. Optimum selection of machining process parameters can improve the environmental impact and production efficiency of the machining process effectively. However, existing studies toward machining process parameters optimisation are focusing on computationally expensive numerical simulations and costly physical models, which are inefficient and labor-expensive. Moreover, the numerical simulations and physical models often show an unsatisfactory accuracy in the actual exploitation stage, which would make the final optimisation solution cannot achieve the best optimum results. Therefore, this paper proposes a deep learning based data-driven genetic algorithm and TOPSIS for multi objective optimisation of machining process parameters and searching the final solutions. First, deep learning is employed in this paper to automatically develop the data-driven prediction function of different optimized objectives. Then the developed optimized objective prediction function is converted into the surrogate model and integrated with the genetic algorithm for generating the Pareto set. Finally, the TOPSIS is employed to automatically search the best optimum processing parameter from the generated Pareto set. The experiments conducted on a milling machine and the experimental results show that the proposed parameters selection method is feasible and effective, and it can effectively and adjustably help operators to realize a balance among the multiple different conflicting objectives.
•Data-driven genetic algorithm is proposed and employed for machining process parameters optimization.•Deep learning is employed to automatically develop the data-driven prediction function of different objectives.•This work provides a cost-efficient way for the operators to select the process parameters of machining process.•Little effort and expert knowledge are required during the optimal selection of process parameters.•The results can help manufacturers to select best optimal energy-efficient machining process in a flexible way. |
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| AbstractList | Machining process is currently widely employed in mechanical manufacturing systems. Optimum selection of machining process parameters can improve the environmental impact and production efficiency of the machining process effectively. However, existing studies toward machining process parameters optimisation are focusing on computationally expensive numerical simulations and costly physical models, which are inefficient and labor-expensive. Moreover, the numerical simulations and physical models often show an unsatisfactory accuracy in the actual exploitation stage, which would make the final optimisation solution cannot achieve the best optimum results. Therefore, this paper proposes a deep learning based data-driven genetic algorithm and TOPSIS for multi objective optimisation of machining process parameters and searching the final solutions. First, deep learning is employed in this paper to automatically develop the data-driven prediction function of different optimized objectives. Then the developed optimized objective prediction function is converted into the surrogate model and integrated with the genetic algorithm for generating the Pareto set. Finally, the TOPSIS is employed to automatically search the best optimum processing parameter from the generated Pareto set. The experiments conducted on a milling machine and the experimental results show that the proposed parameters selection method is feasible and effective, and it can effectively and adjustably help operators to realize a balance among the multiple different conflicting objectives.
•Data-driven genetic algorithm is proposed and employed for machining process parameters optimization.•Deep learning is employed to automatically develop the data-driven prediction function of different objectives.•This work provides a cost-efficient way for the operators to select the process parameters of machining process.•Little effort and expert knowledge are required during the optimal selection of process parameters.•The results can help manufacturers to select best optimal energy-efficient machining process in a flexible way. |
| Author | Wang, Yulin Liu, Xueqian He, Yan Wu, Pengcheng He, Jingsen Li, Yufeng |
| Author_xml | – sequence: 1 givenname: Pengcheng surname: Wu fullname: Wu, Pengcheng organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China – sequence: 2 givenname: Yan surname: He fullname: He, Yan email: heyan@cqu.edu.cn organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China – sequence: 3 givenname: Yufeng surname: Li fullname: Li, Yufeng organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China – sequence: 4 givenname: Jingsen surname: He fullname: He, Jingsen organization: State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China – sequence: 5 givenname: Xueqian surname: Liu fullname: Liu, Xueqian organization: Chongqing Hongyu Precision Industry Group Co. Ltd., Chongqing 402760, China – sequence: 6 givenname: Yulin surname: Wang fullname: Wang, Yulin organization: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China |
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| Cites_doi | 10.1016/j.jmsy.2017.02.011 10.1109/TIE.2018.2877090 10.1109/TIE.2018.2807414 10.1016/j.matpr.2017.07.210 10.1016/j.jclepro.2013.02.030 10.1109/TEVC.2013.2281535 10.1016/j.ymssp.2018.05.050 10.1016/j.jmsy.2017.11.004 10.1016/j.measurement.2019.02.048 10.1016/j.apenergy.2019.02.052 10.1016/j.jmsy.2021.03.023 10.1007/s00170-018-2373-3 10.1016/j.ymssp.2017.05.006 10.1109/TEVC.2018.2834881 10.1007/s00170-017-1032-4 10.1016/j.eswa.2012.05.056 10.1016/j.jmsy.2020.06.009 10.1016/j.ins.2018.10.005 10.1016/S0890-6955(01)00014-1 10.1038/nature14539 10.1016/j.apenergy.2020.115402 10.1016/j.eswa.2015.11.007 10.1016/j.neucom.2017.09.069 10.1016/j.jmsy.2021.03.025 10.1109/TEVC.2013.2281534 10.1007/s40684-019-00029-0 10.1109/TASE.2018.2826362 10.1016/j.jclepro.2012.08.008 10.1016/j.knosys.2014.02.015 10.1109/TIE.2017.2733438 |
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| References | Guo, Lei, Xing, Yan, Li (bib28) 2019; 66 Sreejith, Ngoi (bib1) 2001; 41 Bhushan (bib4) 2013; 39 Rajesh, Yohan, Venkataramaiah, Pallavi (bib18) 2017; 4 Luo, Wang, Liu, Li, Peng (bib25) 2019; 66 Jain, Deb (bib29) 2014; 18 Xiao, Jiang, Gu, Yan, Wang (bib15) 2021; 59 Fan, Sun, Zhao, Song, Wang (bib23) 2019; 240 Sadeghifar, Sedaghati, Jomaa, Songmene (bib16) 2018; 94 Awad, Hassan (bib14) 2018; 46 Giampieri, Ling-Chin, Ma, Smallbone, Roskilly (bib21) 2020 Tavana, Li, Mobin, Komaki, Teymourian (bib32) 2016; 50 Lee, Huang, Ji, Lin (bib5) 2018; 15 Behzadian, Otaghsara, Yazdani, Ignatius (bib33) 2012; 39 Serra, Chibane, Duchosal (bib7) 2018; 99 Wang, Jin, Sun, Doherty (bib13) 2019; 23 Deng, Du, Jia, Zhao, Xie (bib20) 2020; 56 Wu, Liu, Zhang, Terpenny, Gao, Kurfess (bib6) 2017; 43 Muaz, Choudhury (bib12) 2019; 138 Prakash, Gopal, Karthik (bib9) 2020 Zhao, Wang, Yan, Mao, Shen, Wang (bib24) 2018; 65 Karandikar, Schmitz, Smith (bib19) 2021; 59 LeCun, Bengio, Hinton (bib26) 2015; 521 Lu (bib17) 2019; 33 He, Wu, Li, Wang, Tao, Wang (bib35) 2020; 275 Abdeljaber, Avci, Kiranyaz, Boashash, Sodano, Inman (bib27) 2018; 275 Schneider, Krohling (bib34) 2014; 62 Plaza, Lopez (bib2) 2018; 98 Muaz, Choudhury (bib11) 2019; 138 Deb, Jain (bib30) 2014; 18 Ghosh, Martinsen (bib8) 2020; 23 Yan, Li (bib10) 2013; 52 Deng, Lv, Huang, Shi (bib3) 2019; 6 Yi, Xing, Wang, Dong, Vasilakos, Alavi (bib31) 2020; 509 Zhao, Yan, Chen, Mao, Wang, Gao (bib22) 2019; 115 Deb (10.1016/j.jmsy.2022.05.016_bib30) 2014; 18 Giampieri (10.1016/j.jmsy.2022.05.016_bib21) 2020 Behzadian (10.1016/j.jmsy.2022.05.016_bib33) 2012; 39 Muaz (10.1016/j.jmsy.2022.05.016_bib11) 2019; 138 Zhao (10.1016/j.jmsy.2022.05.016_bib22) 2019; 115 Guo (10.1016/j.jmsy.2022.05.016_bib28) 2019; 66 Xiao (10.1016/j.jmsy.2022.05.016_bib15) 2021; 59 Lee (10.1016/j.jmsy.2022.05.016_bib5) 2018; 15 Prakash (10.1016/j.jmsy.2022.05.016_bib9) 2020 Zhao (10.1016/j.jmsy.2022.05.016_bib24) 2018; 65 Schneider (10.1016/j.jmsy.2022.05.016_bib34) 2014; 62 Rajesh (10.1016/j.jmsy.2022.05.016_bib18) 2017; 4 Plaza (10.1016/j.jmsy.2022.05.016_bib2) 2018; 98 Deng (10.1016/j.jmsy.2022.05.016_bib20) 2020; 56 Karandikar (10.1016/j.jmsy.2022.05.016_bib19) 2021; 59 Yan (10.1016/j.jmsy.2022.05.016_bib10) 2013; 52 Deng (10.1016/j.jmsy.2022.05.016_bib3) 2019; 6 Jain (10.1016/j.jmsy.2022.05.016_bib29) 2014; 18 Awad (10.1016/j.jmsy.2022.05.016_bib14) 2018; 46 Abdeljaber (10.1016/j.jmsy.2022.05.016_bib27) 2018; 275 Wu (10.1016/j.jmsy.2022.05.016_bib6) 2017; 43 LeCun (10.1016/j.jmsy.2022.05.016_bib26) 2015; 521 Serra (10.1016/j.jmsy.2022.05.016_bib7) 2018; 99 He (10.1016/j.jmsy.2022.05.016_bib35) 2020; 275 Muaz (10.1016/j.jmsy.2022.05.016_bib12) 2019; 138 Sreejith (10.1016/j.jmsy.2022.05.016_bib1) 2001; 41 Ghosh (10.1016/j.jmsy.2022.05.016_bib8) 2020; 23 Wang (10.1016/j.jmsy.2022.05.016_bib13) 2019; 23 Sadeghifar (10.1016/j.jmsy.2022.05.016_bib16) 2018; 94 Bhushan (10.1016/j.jmsy.2022.05.016_bib4) 2013; 39 Fan (10.1016/j.jmsy.2022.05.016_bib23) 2019; 240 Luo (10.1016/j.jmsy.2022.05.016_bib25) 2019; 66 Lu (10.1016/j.jmsy.2022.05.016_bib17) 2019; 33 Yi (10.1016/j.jmsy.2022.05.016_bib31) 2020; 509 Tavana (10.1016/j.jmsy.2022.05.016_bib32) 2016; 50 |
| References_xml | – volume: 275 year: 2020 ident: bib35 article-title: A generic energy prediction model of machine tools using deep learning algorithms publication-title: Appl Energy – volume: 59 start-page: 522 year: 2021 end-page: 534 ident: bib19 article-title: Physics-guided logistic classification for tool life modeling and process parameter optimisation in machining(z.star) publication-title: J Manuf Syst – start-page: 261 year: 2020 ident: bib21 article-title: A review of the current automotive manufacturing practice from an energy perspective publication-title: Appl Energy – volume: 18 start-page: 602 year: 2014 end-page: 622 ident: bib29 article-title: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: handling constraints and extending to an adaptive approach publication-title: IEEE Trans Evol Comput – volume: 41 start-page: 1831 year: 2001 end-page: 1843 ident: bib1 article-title: Material removal mechanisms in precision machining of new materials publication-title: Int J Mach TOOLS Manuf – volume: 56 start-page: 359 year: 2020 end-page: 372 ident: bib20 article-title: Prognostic study of ball screws by ensemble data-driven particle filters publication-title: J Manuf Syst – volume: 275 start-page: 1308 year: 2018 end-page: 1317 ident: bib27 article-title: 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data publication-title: Neurocomputing – volume: 98 start-page: 634 year: 2018 end-page: 651 ident: bib2 article-title: Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning publication-title: Mech Syst Signal Process – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib26 article-title: Deep learning publication-title: Nature – volume: 115 start-page: 213 year: 2019 end-page: 237 ident: bib22 article-title: Deep learning and its applications to machine health monitoring publication-title: Mech Syst Signal Process – volume: 50 start-page: 17 year: 2016 end-page: 39 ident: bib32 article-title: Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS publication-title: Expert Syst Appl – volume: 66 start-page: 509 year: 2019 end-page: 518 ident: bib25 article-title: Early fault detection of machine tools based on deep learning and dynamic identification publication-title: IEEE Trans Ind Electron – volume: 15 start-page: 1665 year: 2018 end-page: 1676 ident: bib5 article-title: An online tool temperature monitoring method based on physics-guided infrared image features and artificial neural network for dry cutting publication-title: IEEE Trans Autom Sci Eng – volume: 66 start-page: 7316 year: 2019 end-page: 7325 ident: bib28 article-title: Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data publication-title: IEEE Trans Ind Electron – volume: 509 start-page: 470 year: 2020 end-page: 487 ident: bib31 article-title: Behavior of crossover operators in NSGA-III for large-scale optimization problems publication-title: Inf Sci – volume: 23 start-page: 650 year: 2020 end-page: 663 ident: bib8 article-title: Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms publication-title: Eng Sci Technol Int J JESTECH – volume: 43 start-page: 25 year: 2017 end-page: 34 ident: bib6 article-title: A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing publication-title: J Manuf Syst – volume: 99 start-page: 2025 year: 2018 end-page: 2034 ident: bib7 article-title: Multi-objective optimization of cutting parameters for turning AISI 52100 hardened steel publication-title: Int J Adv Manuf Technol – volume: 33 start-page: 208 year: 2019 end-page: 215 ident: bib17 article-title: A thermal FEA modeling of multiple machining processes for practical machining process optimisation publication-title: Sustain Manuf Glob Circ Econ – start-page: 157 year: 2020 ident: bib9 article-title: Multi-objective optimization using Taguchi based grey relational analysis in turning of Rock dust reinforced aluminum MMC publication-title: Measurement – volume: 39 start-page: 13051 year: 2012 end-page: 13069 ident: bib33 article-title: A state-of the-art survey of TOPSIS applications publication-title: Expert Syst Appl – volume: 138 start-page: 557 year: 2019 end-page: 569 ident: bib11 article-title: Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel publication-title: Measurement – volume: 6 start-page: 23 year: 2019 end-page: 41 ident: bib3 article-title: A high efficiency and low carbon oriented machining process route optimization model and its application publication-title: Int J Precis Eng Manuf Technol – volume: 138 start-page: 557 year: 2019 end-page: 569 ident: bib12 article-title: Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel publication-title: Measurement – volume: 59 start-page: 535 year: 2021 end-page: 548 ident: bib15 article-title: A novel approach to CNC machining center processing parameters optimisation considering energy-saving and low-cost publication-title: J Manuf Syst – volume: 65 start-page: 1539 year: 2018 end-page: 1548 ident: bib24 article-title: Machine health monitoring using local feature-based gated recurrent unit networks publication-title: IEEE Trans Ind Electron – volume: 39 start-page: 242 year: 2013 end-page: 254 ident: bib4 article-title: Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites publication-title: J Clean Prod – volume: 4 start-page: 8624 year: 2017 end-page: 8632 ident: bib18 article-title: Optimization of cutting parameters for minimization of cutting temperature and surface roughness in turning of Al6061 alloy publication-title: Mater Today Proc – volume: 240 start-page: 35 year: 2019 end-page: 45 ident: bib23 article-title: Deep learning-based feature engineering methods for improved building energy prediction publication-title: Appl Energy – volume: 46 start-page: 79 year: 2018 end-page: 92 ident: bib14 article-title: Joint decisions of machining process parameters setting and lot-size determination with environmental and quality cost consideration publication-title: J Manuf Syst – volume: 23 start-page: 203 year: 2019 end-page: 216 ident: bib13 article-title: Offline data-driven evolutionary optimization using selective surrogate ensembles publication-title: IEEE Trans Evol Comput – volume: 52 start-page: 462 year: 2013 end-page: 471 ident: bib10 article-title: Multi-objective optimization of milling parameters - the trade-offs between energy, production rate and cutting quality publication-title: J Clean Prod – volume: 18 start-page: 577 year: 2014 end-page: 601 ident: bib30 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: solving problems with box constraints publication-title: IEEE Trans Evol Comput – volume: 94 start-page: 2457 year: 2018 end-page: 2474 ident: bib16 article-title: Finite element analysis and response surface method for robust multi-performance optimization of radial turning of hard 300M steel publication-title: Int J Adv Manuf Technol – volume: 62 start-page: 47 year: 2014 end-page: 56 ident: bib34 article-title: A hybrid approach using TOPSIS, differential evolution, and Tabu search to find multiple solutions of constrained non-linear integer optimization problems publication-title: Knowledge Based Syst – volume: 43 start-page: 25 year: 2017 ident: 10.1016/j.jmsy.2022.05.016_bib6 article-title: A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2017.02.011 – volume: 23 start-page: 650 year: 2020 ident: 10.1016/j.jmsy.2022.05.016_bib8 article-title: Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms publication-title: Eng Sci Technol Int J JESTECH – volume: 66 start-page: 7316 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib28 article-title: Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2018.2877090 – volume: 66 start-page: 509 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib25 article-title: Early fault detection of machine tools based on deep learning and dynamic identification publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2018.2807414 – volume: 4 start-page: 8624 year: 2017 ident: 10.1016/j.jmsy.2022.05.016_bib18 article-title: Optimization of cutting parameters for minimization of cutting temperature and surface roughness in turning of Al6061 alloy publication-title: Mater Today Proc doi: 10.1016/j.matpr.2017.07.210 – volume: 33 start-page: 208 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib17 article-title: A thermal FEA modeling of multiple machining processes for practical machining process optimisation publication-title: Sustain Manuf Glob Circ Econ – volume: 52 start-page: 462 year: 2013 ident: 10.1016/j.jmsy.2022.05.016_bib10 article-title: Multi-objective optimization of milling parameters - the trade-offs between energy, production rate and cutting quality publication-title: J Clean Prod doi: 10.1016/j.jclepro.2013.02.030 – volume: 18 start-page: 577 year: 2014 ident: 10.1016/j.jmsy.2022.05.016_bib30 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: solving problems with box constraints publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2013.2281535 – volume: 115 start-page: 213 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib22 article-title: Deep learning and its applications to machine health monitoring publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2018.05.050 – volume: 46 start-page: 79 year: 2018 ident: 10.1016/j.jmsy.2022.05.016_bib14 article-title: Joint decisions of machining process parameters setting and lot-size determination with environmental and quality cost consideration publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2017.11.004 – start-page: 261 year: 2020 ident: 10.1016/j.jmsy.2022.05.016_bib21 article-title: A review of the current automotive manufacturing practice from an energy perspective publication-title: Appl Energy – volume: 138 start-page: 557 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib12 article-title: Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel publication-title: Measurement doi: 10.1016/j.measurement.2019.02.048 – volume: 240 start-page: 35 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib23 article-title: Deep learning-based feature engineering methods for improved building energy prediction publication-title: Appl Energy doi: 10.1016/j.apenergy.2019.02.052 – volume: 59 start-page: 535 year: 2021 ident: 10.1016/j.jmsy.2022.05.016_bib15 article-title: A novel approach to CNC machining center processing parameters optimisation considering energy-saving and low-cost publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2021.03.023 – volume: 99 start-page: 2025 year: 2018 ident: 10.1016/j.jmsy.2022.05.016_bib7 article-title: Multi-objective optimization of cutting parameters for turning AISI 52100 hardened steel publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-018-2373-3 – volume: 98 start-page: 634 year: 2018 ident: 10.1016/j.jmsy.2022.05.016_bib2 article-title: Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.05.006 – volume: 23 start-page: 203 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib13 article-title: Offline data-driven evolutionary optimization using selective surrogate ensembles publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2018.2834881 – volume: 94 start-page: 2457 year: 2018 ident: 10.1016/j.jmsy.2022.05.016_bib16 article-title: Finite element analysis and response surface method for robust multi-performance optimization of radial turning of hard 300M steel publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-017-1032-4 – volume: 39 start-page: 13051 year: 2012 ident: 10.1016/j.jmsy.2022.05.016_bib33 article-title: A state-of the-art survey of TOPSIS applications publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2012.05.056 – volume: 56 start-page: 359 year: 2020 ident: 10.1016/j.jmsy.2022.05.016_bib20 article-title: Prognostic study of ball screws by ensemble data-driven particle filters publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2020.06.009 – volume: 509 start-page: 470 year: 2020 ident: 10.1016/j.jmsy.2022.05.016_bib31 article-title: Behavior of crossover operators in NSGA-III for large-scale optimization problems publication-title: Inf Sci doi: 10.1016/j.ins.2018.10.005 – volume: 41 start-page: 1831 year: 2001 ident: 10.1016/j.jmsy.2022.05.016_bib1 article-title: Material removal mechanisms in precision machining of new materials publication-title: Int J Mach TOOLS Manuf doi: 10.1016/S0890-6955(01)00014-1 – volume: 521 start-page: 436 year: 2015 ident: 10.1016/j.jmsy.2022.05.016_bib26 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 138 start-page: 557 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib11 article-title: Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel publication-title: Measurement doi: 10.1016/j.measurement.2019.02.048 – volume: 275 year: 2020 ident: 10.1016/j.jmsy.2022.05.016_bib35 article-title: A generic energy prediction model of machine tools using deep learning algorithms publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115402 – volume: 50 start-page: 17 year: 2016 ident: 10.1016/j.jmsy.2022.05.016_bib32 article-title: Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.11.007 – start-page: 157 year: 2020 ident: 10.1016/j.jmsy.2022.05.016_bib9 article-title: Multi-objective optimization using Taguchi based grey relational analysis in turning of Rock dust reinforced aluminum MMC publication-title: Measurement – volume: 275 start-page: 1308 year: 2018 ident: 10.1016/j.jmsy.2022.05.016_bib27 article-title: 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.09.069 – volume: 59 start-page: 522 year: 2021 ident: 10.1016/j.jmsy.2022.05.016_bib19 article-title: Physics-guided logistic classification for tool life modeling and process parameter optimisation in machining(z.star) publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2021.03.025 – volume: 18 start-page: 602 year: 2014 ident: 10.1016/j.jmsy.2022.05.016_bib29 article-title: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: handling constraints and extending to an adaptive approach publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2013.2281534 – volume: 6 start-page: 23 year: 2019 ident: 10.1016/j.jmsy.2022.05.016_bib3 article-title: A high efficiency and low carbon oriented machining process route optimization model and its application publication-title: Int J Precis Eng Manuf Technol doi: 10.1007/s40684-019-00029-0 – volume: 15 start-page: 1665 year: 2018 ident: 10.1016/j.jmsy.2022.05.016_bib5 article-title: An online tool temperature monitoring method based on physics-guided infrared image features and artificial neural network for dry cutting publication-title: IEEE Trans Autom Sci Eng doi: 10.1109/TASE.2018.2826362 – volume: 39 start-page: 242 year: 2013 ident: 10.1016/j.jmsy.2022.05.016_bib4 article-title: Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites publication-title: J Clean Prod doi: 10.1016/j.jclepro.2012.08.008 – volume: 62 start-page: 47 year: 2014 ident: 10.1016/j.jmsy.2022.05.016_bib34 article-title: A hybrid approach using TOPSIS, differential evolution, and Tabu search to find multiple solutions of constrained non-linear integer optimization problems publication-title: Knowledge Based Syst doi: 10.1016/j.knosys.2014.02.015 – volume: 65 start-page: 1539 year: 2018 ident: 10.1016/j.jmsy.2022.05.016_bib24 article-title: Machine health monitoring using local feature-based gated recurrent unit networks publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2017.2733438 |
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