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Energy optimization method for variable curvature contour machining

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Abstract

Energy saving and consumption reduction is one of the current important research in the field of green and sustainable manufacturing. Products or components containing variable curvature contouring are widely used in the automotive, medical, aerospace, and mold industries, while there is a lack of methods to model the energy consumption ratio for variable curvature contouring and improve its energy efficiency. A method for modeling the specific energy consumption of variable curvature contouring and energy consumption optimization is proposed for this problem. Firstly, the components of energy consumption in processing of the CNC machine tool machining are analyzed, and the relationship between curvature characteristics and material removal rate is investigated from the geometric perspective. Secondly, orthogonal experiments with different curvatures of straight lines, convex arcs, and concave arcs are designed to collect energy consumption data. Based on the experimental data, the Dueling Deep Q-Network optimization support vector regression (Dueling DQN-SVR) was used to establish the specific energy consumption model considering the curvature. Finally, a multi-objective optimization model is constructed when considering specific energy consumption, efficiency, and quality, and the Pareto solution set is solved using a multi-objective Gray Wolf optimization algorithm (MOGWO). The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to select the optimal combination of machining parameters. The experimental results show that the accuracy of the established model is more than 95%. The method improved energy efficiency by more than 7.82% and efficiency by more than 1.128%. These research results are of great theoretical and practical significance for achieving energy-efficient variable curvature contour machining.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

References

  1. Joung CB, Carrell J, Sarkar P, Feng SC (2013) Categorization of indicators for sustainable manufacturing. Ecol Ind 24:148–157. https://doi.org/10.1016/j.ecolind.2012.05.030

    Article  Google Scholar 

  2. Liao W, Garg A, Gao L (2018) Design of robust energy consumption model for manufacturing process considering uncertainties. J Clean Prod 172:119–132. https://doi.org/10.1016/j.jclepro.2017.10.155

    Article  Google Scholar 

  3. OECD I (2016) Energy and air pollution: world energy outlook special report 2016

  4. Balogun VA, Mativenga PT (2013) Modelling of direct energy requirements in mechanical machining processes. J Clean Prod 41:179–186. https://doi.org/10.1016/j.jclepro.2012.10.015

    Article  Google Scholar 

  5. Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Ann 60(1):37–40. https://doi.org/10.1016/j.cirp.2011.03.018

    Article  Google Scholar 

  6. He Y, Liu B, Zhang X, Gao H, Liu X (2012) A modeling method of task-oriented energy consumption for machining manufacturing system. J Clean Prod 23(1):167–174. https://doi.org/10.1016/j.jclepro.2011.10.033

    Article  Google Scholar 

  7. Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. In: 13th CIRP international conference on life cycle engineering (Vol 31, No. 1, pp 623–638). Leuven, Belgium

  8. Liu ZY, Sealy MP, Li W, Zhang D, Fang XY, Guo YB, Liu ZQ (2018) Energy consumption characteristics in finish hard milling. J Manuf Process 35:500–507. https://doi.org/10.1016/j.jmapro.2018.08.036

    Article  Google Scholar 

  9. Jia S, Yuan Q, Lv J, Liu Y, Ren D, Zhang Z (2017) Therblig-embedded value stream mapping method for lean energy machining. Energy 138:1081–1098. https://doi.org/10.1016/j.energy.2017.07.120

    Article  Google Scholar 

  10. Hongchao Zhang, Lulu Kong, Tao Li, Junchao Chen (2015) SCE modeling and influencing trend analysis of cutting parameters. China Mech Eng 26(8):1098–1104. https://doi.org/10.3969/j.issn.1004-132X.2015.08.019

    Article  Google Scholar 

  11. de Carvalho HMB, de Gomes JO, Schmidt MA, Brandão VLC (2015) Vibration analysis and energy efficiency in interrupted face milling processes. 22nd CIRP Conf Life Cycle Eng 29:245–250

    Google Scholar 

  12. Pervaiz S, Deiab I, Rashid A, Nicolescu M (2015) Prediction of energy consumption and environmental implications for turning operation using finite element analysis. Proc Inst Mech Eng Part B: J Eng Manuf 229(11):1925–1932. https://doi.org/10.1177/0954405414541105

    Article  Google Scholar 

  13. Li W, Kara S (2011) An empirical model for predicting energy consumption of manufacturing processes: A case of turning process. Proc Inst Mech Eng Part B: J Eng Manuf 225(9):1636–1646. https://doi.org/10.1177/2041297511398541

    Article  Google Scholar 

  14. Li L, Yan J, Xing Z (2013) Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. J Clean Prod 52:113–121. https://doi.org/10.1016/j.jclepro.2013.02.039

    Article  Google Scholar 

  15. Lv J, Tang R, Jia S, Liu Y (2016) Experimental study on energy consumption of computer numerical control machine tools. J Clean Prod 112:3864–3874. https://doi.org/10.1016/j.jclepro.2015.07.040

    Article  Google Scholar 

  16. Xie N, Zhou J, Zheng B (2018) Selection of optimum turning parameters based on cooperative optimization of minimum energy consumption and high surface quality. In Wang L (Ed.), 51ST CIRP CONFERENCE ON MANUFACTURING SYSTEMS (Vol. 72, pp. 1469–1474). Elsevier Science Bv. https://doi.org/10.1016/j.procir.2018.03.099

  17. Li B, Tian X, Zhang M (2020) Modeling and multi-objective optimization of cutting parameters in the high-speed milling using RSM and improved TLBO algorithm. Int J Adv Manuf Technol 111(7):2323–2335. https://doi.org/10.1007/s00170-020-06284-9

    Article  Google Scholar 

  18. Wu P, He Y, Li Y, He J, Liu X, Wang Y (2022) Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS. J Manuf Syst 64:40–52. https://doi.org/10.1016/j.jmsy.2022.05.016

    Article  Google Scholar 

  19. Feng C, Guo H, Zhang J, Huang Y, Huang S (2022) A systematic method of optimization of machining parameters considering energy consumption, machining time, and surface roughness with experimental analysis. Int J Adv Manuf Technol 119(11):7383–7401. https://doi.org/10.1007/s00170-022-08772-6

    Article  Google Scholar 

  20. Xiao Y, Zhang H, Jiang Z, Gu Q, Yan W (2021) Multiobjective optimization of machining center process route: Tradeoffs between energy and cost. J Clean Prod 280:124171. https://doi.org/10.1016/j.jclepro.2020.124171

    Article  Google Scholar 

  21. Wang Z, Schaul T, Hessel M, Hasselt H, Lanctot M, Freitas N (2016) Dueling network architectures for deep reinforcement learning. In Balcan MF, Weinberger KQ (Eds.), Proceedings of The 33rd International Conference on Machine Learning (Vol. 48, pp. 1995–2003). PMLR. https://doi.org/10.48550/arXiv.1511.06581

  22. Chaoyi C, Juan LU, Kai C, Yujia LI, Junyan MA, Xiaoping L (2021) Research on analytical model and DDQN-SVR prediction model of turning surface roughness. J Mech Eng 57(13):262–272. https://doi.org/10.3901/JME.2021.13.262

    Article  Google Scholar 

  23. Rao VS, Rao PVM (2006) Effect of workpiece curvature on cutting forces and surface error in peripheral milling. Proc Inst Mech Eng Part B: J Eng Manuf 220(9):1399–1407. https://doi.org/10.1243/09544054JEM397

    Article  Google Scholar 

  24. Ni H, Yan C, Cao W, Liu Y (2020) A novel parameter decision approach in hobbing process for minimizing carbon footprint and processing time. Int J Adv Manuf Technol 111(11):3405–3419. https://doi.org/10.1007/s00170-020-06103-1

    Article  Google Scholar 

  25. Hengxin NI, Chunping YAN, Jianlin C, Yuehui HOU, Liang C (2021) Multi-objective optimization and decision-making method of high speed dry gear hobbing processing parameters. China Mech Eng 32(7):832. https://doi.org/10.3969/j.issn.1004-132X.2021.07.010

    Article  Google Scholar 

  26. Yoon K (1987) A reconciliation among discrete compromise solutions. J Oper Res Soc 38(3):277–286. https://doi.org/10.1057/jors.1987.44

    Article  Google Scholar 

  27. Hwang C-L, Lai Y-J, Liu T-Y (1993) A new approach for multiple objective decision making. Comput Oper Res 20(8):889–899. https://doi.org/10.1016/0305-0548(93)90109-V

    Article  Google Scholar 

Download references

Funding

This research is supported by the National Natural Science Foundation of China (NSFC) (Grant No. 52165062), Guangxi Natural Science Foundation Program (Grant No. 2020JJD160004).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology, Guangxi University, Nanning, 530004, China

    Junyan Ma, Jiangyou Liu, Lutao Wei, Xiaoping Liao & Chengyi Ou

  2. Department of Mechanical and Marine Engineering, Beibu Gulf University, Qinzhou, 535011, China

    Juan Lu

Authors
  1. Junyan Ma
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  2. Jiangyou Liu
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  4. Juan Lu
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Contributions

All authors contributed to the study’s conception and design. Junyan Ma is responsible for theoretical analysis, literature search, experimental design and execution, and manuscript writing. Jiangyou Liu, Lutao Wei, Chengyi Ou, and Juan Lu are primarily responsible for guiding the content and structure of the paper as well as reviewing and assisting in the revision of the manuscript. Liao is the general leader of the grant project, responsible for controlling the research direction and methodology and reviewing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jiangyou Liu.

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Ma, J., Liu, J., Wei, L. et al. Energy optimization method for variable curvature contour machining. Int J Adv Manuf Technol 132, 2187–2207 (2024). https://doi.org/10.1007/s00170-024-13478-y

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