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A cutting parameter energy-saving optimization method considering tool wear for multi-feature parts batch processing

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Abstract

Cutting parameters and tool wear both have significant influence on energy consumption in the processing. In a multi-feature parts batch processing, tool wear values are continuously increasing with the proceeding of processing, leading to a higher energy consumption. To reduce the wear speed, cutting parameters should be continuously adjusted according to different states of tool wear during batch processing. However, current cutting parameter optimization studies only focus on one specific workpiece and the tool wear is seldom considered in the batch processing. To fill this research gap, a cutting parameter energy-saving optimization method considering tool wear for multi-feature parts batch processing was proposed in this paper. First, the synergistic effect mechanism of cutting parameters and tool wear on energy consumption in the batch processing was analyzed. On this basis, a multi-objective cutting parameter optimization model for multi-feature parts batch processing was established. Then, the multi-objective cuckoo search (MOCS) algorithm was used to solve the optimization model. Finally, an experimental case was carried out to verify the effectiveness and practicability of the proposed method. Results show that energy consumption and machining time can be, respectively, decreased by 22.9% and 4.1%. Meanwhile, a conflict relationship exists between the energy consumption and machining time in the processing and the trade-off of them is analyzed in this paper.

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References

  1. Spiering T, Kohlitz S, Sundmaeker H, Herrmann C (2015) Energy efficiency benchmarking for injection moulding processes. Rob Comput Integr Manuf 36:45–59. https://doi.org/10.1016/j.rcim.2014.12.010

    Article  Google Scholar 

  2. International Energy Agency (IEA) (2019) International energy outlook 2019. Retrieved 15 Mar 2021 from https://www.eia.gov/outlooks/ieo/pdf/ieo2019.pdf

  3. Park CW, Kwon KS, Kim WB, Min BK, Park SJ, Sung IH, Yoon YS, Lee KS, Lee JH, Seok J (2009) Energy consumption reduction technology in manufacturing—a selective review of policies, standards, and research. Int J Precis Eng Manuf 10:151–173. https://doi.org/10.1007/s12541-009-0107-z

    Article  Google Scholar 

  4. Xiao Q, Li C, Tang Y, Li L (2019) Meta-reinforcement learning of machining parameters for energy-efficient process control of flexible turning operations. IEEE Trans Autom Sci Eng 99:1–4. https://doi.org/10.1109/TASE.2019.2924444

    Article  Google Scholar 

  5. Xie J, Cai W, Du Y, Tang Y, Tuo J (2021) Modelling approach for energy efficiency of machining system based on torque model and angular velocity. J Clean Prod 293:126249. https://doi.org/10.1016/j.jclepro.2021.126249

    Article  Google Scholar 

  6. Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. Proc CIRP Int Conf Life Cycle Eng 5–11

  7. Zhao X, Li C, Chen X, Cui J, Cao B (2021) Data-driven cutting parameters optimization method in multiple configurations machining process for energy consumption and production time saving. Int J Precis Eng Manuf Green Technol 1–20. https://doi.org/10.1007/s40684-021-00373-0

  8. Newman ST, Nassehi A, Imani-Asrai R, Dhokia V (2012) Energy efficient process planning for CNC machining. CIRP J Manuf Sci Technol 5:127–136. https://doi.org/10.1016/j.cirpj.2012.03.007

    Article  Google Scholar 

  9. Hu L, Tang R, Cai W, Feng Y, Ma X (2019) Optimisation of cutting parameters for improving energy efficiency in machining process. Robot Comput Integr Manuf 59:406–416. https://doi.org/10.1016/j.rcim.2019.04.015

    Article  Google Scholar 

  10. Li C, Xiao Q, Tang Y, Li L (2016) A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving. J Clean Prod 135:263–275. https://doi.org/10.1016/j.jclepro.2016.06.097

    Article  Google Scholar 

  11. Bhushan RK (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242–254. https://doi.org/10.1016/j.jclepro.2012.08.008

    Article  Google Scholar 

  12. Cui X, Guo J (2018) Identification of the optimum cutting parameters in intermittent hard turning with specific cutting energy, damage equivalent stress, and surface roughness considered. Int J Adv Manuf Technol 96:4281–4293. https://doi.org/10.1007/s00170-018-1885-1

    Article  Google Scholar 

  13. Chen X, Li C, Tang Y, Li L, Du Y, Li L (2019) Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time. Energy 175:1021–1037. https://doi.org/10.1016/j.energy.2019.02.157

    Article  Google Scholar 

  14. Moreira LC, Li WD, Lu X, Fitzpatrick ME (2019) Energy-Efficient machining process analysis and optimisation based on BS EN24T alloy steel as case studies. Rob Comput Integr Manuf 58:1–12. https://doi.org/10.1016/j.rcim.2019.01.011

    Article  Google Scholar 

  15. Zhang G, Guo C (2016) Modeling flank wear progression based on cutting force and energy prediction in turning process. Procedia Manuf 5:536–545. https://doi.org/10.1016/j.promfg.2016.08.044

    Article  Google Scholar 

  16. Yoon HS, Lee JY, Kim MS, Ahn SH (2014) Empirical power-consumption model for material removal in three-axis milling. J Clean Prod 78:54–62. https://doi.org/10.1016/j.jclepro.2014.03.061

    Article  Google Scholar 

  17. Tian C, Zhou G, Zhang J, Zhang C (2019) Optimization of cutting parameters considering tool wear conditions in low-carbon manufacturing environment. J Clean Prod 226:706–719. https://doi.org/10.1016/j.jclepro.2019.04.113

    Article  Google Scholar 

  18. Zhou G, Yuan S, Lu Q, Xiao X (2018) A carbon emission quantitation model and experimental evaluation for machining process considering tool wear condition. Int J Adv Manuf Technol 98:565–577. https://doi.org/10.1007/s00170-018-2281-6

    Article  Google Scholar 

  19. Bagaber SA, Yusoff AR (2017) Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316. J Clean Prod 157:30–46. https://doi.org/10.1016/j.jclepro.2017.03.231

    Article  Google Scholar 

  20. Xie N, Zhou J, Zheng B (2018) Selection of optimum turning parameters based on cooperative optimization of minimum energy consumption and high surface quality. Procedia CIRP 72:1469–1474. https://doi.org/10.1016/j.procir.2018.03.099

    Article  Google Scholar 

  21. Zhang X, Yu T, Dai Y, Qu S, Zhao J (2020) Energy consumption considering tool wear and optimization of cutting parameters in micro milling process. Int J Mech Sci 178:105628. https://doi.org/10.1016/j.ijmecsci.2020.105628

    Article  Google Scholar 

  22. Wan T, Chen X, Li C, Tang Y (2018) An on-line tool wear monitoring method based on cutting power. IEEE Int Conf Autom Sci Eng 205–210. https://doi.org/10.1109/COASE.2018.8560412

  23. Shi K, Zhang D, Liu N, Wang S, Ren J (2018) A novel energy consumption model for milling process considering tool wear progression. J Clean Prod 184:152–159. https://doi.org/10.1016/j.jclepro.2018.02.239

    Article  Google Scholar 

  24. Wang P, Gao R (2016) Stochastic tool wear prediction for sustainable manufacturing. Procedia CIRP 48:236–241. https://doi.org/10.1016/j.procir.2016.03.101

    Article  Google Scholar 

  25. Selvaraj DP, Chandramohan P, Mohanraj M (2014) Optimization of surface roughness, cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method. Measurement 49:205–215. https://doi.org/10.1016/j.measurement.2013.11.037

    Article  Google Scholar 

  26. Liu Z, Guo Y, Sealy MP, Liu Z (2016) Energy consumption and process sustainability of hard milling with tool wear progression. J Mater Process Technol 229:305–312. https://doi.org/10.1016/j.jmatprotec.2015.09.032

    Article  Google Scholar 

  27. Zhao G, Su Y, Zheng G, Zhao Y, Li C (2020) Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling. P I Mech Eng B-J Eng 234:1346–1354. https://doi.org/10.1177/0954405420911298

    Article  Google Scholar 

  28. Priarone PC, Robiglio M, Settineri L, Tebaldo V (2016) Modelling of specific energy requirements in machining as a function of tool and lubricoolant usage. CIRP Ann 65:25–28. https://doi.org/10.1016/j.cirp.2016.04.108

    Article  Google Scholar 

  29. Albertelli P (2017) Energy saving opportunities in direct drive machine tool spindles. J Clean Prod 165:855–873. https://doi.org/10.1016/j.jclepro.2017.07.175

    Article  Google Scholar 

  30. Mellal MA, Williams EJ (2014) Cuckoo optimization algorithm for unit production cost in multi-pass turning operations. Int J Adv Manuf Technol 76:647–656. https://doi.org/10.1007/s00170-014-6309-2

    Article  Google Scholar 

  31. Zamani AA, Tavakoli S, Etedali S (2017) Fractional order PID control design for semi-active control of smart base-isolated structures: a multi-objective cuckoo search approach. Isa Trans 67:222–232. https://doi.org/10.1016/j.isatra.2017.01.012

    Article  Google Scholar 

  32. Yang XS, Deb S (2009) Cuckoo search via Levy flights. World Congr Nature Biol Inspired Comput NaBIC 210–214. https://doi.org/10.1109/NABIC.2009.5393690

  33. Arriaza OV, Kim DW, Lee DY, Suhaimi MA (2017) Trade-off analysis between machining time and energy consumption in impeller NC machining. Rob Comput Integr Manuf 43:164–170. https://doi.org/10.1016/j.rcim.2015.09.014

    Article  Google Scholar 

  34. Wang W, Tian G, Yuan G, Pham DT (2021) Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm. J Intell Manuf 1–19. https://doi.org/10.1007/s10845-021-01837-5

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Funding

This work was supported in part by the National Key R&D Program of China (No.2019YFB1706103), National Natural Science Foundation of China (No.51975075) and Chongqing Technology Innovation and Application Program (No. cstc2020jscx-msxmX0221).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Congbo Li, Shaoqing Wu, Qian Yi & Xikun Zhao

  2. Chongqing Institute of Quality & Standardization, Chongqing, 400023, China

    Longguo Cui

Authors
  1. Congbo Li
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  2. Shaoqing Wu
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  3. Qian Yi
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  4. Xikun Zhao
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Contributions

Congbo Li and Shaoqing Wu designed the work, performed the research and analyzed the data. Congbo Li, Shaoqing Wu and Qian Yi discussed the results and wrote the manuscript. All authors contributed to conducting experiment, drafting and revising the manuscript.

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Correspondence to Qian Yi.

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Li, C., Wu, S., Yi, Q. et al. A cutting parameter energy-saving optimization method considering tool wear for multi-feature parts batch processing. Int J Adv Manuf Technol 121, 4941–4960 (2022). https://doi.org/10.1007/s00170-022-09557-7

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  • DOI: https://doi.org/10.1007/s00170-022-09557-7

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