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Prediction and optimization of surface roughness in high-speed dry milling of 30CrMnSiNiA using GPR and MOHHO algorithm

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Abstract

Cutting parameters significantly influence the surface roughness of high-speed dry milling (HSDM) of high-strength steel materials. Thus, accurate prediction of surface roughness and optimization of cutting parameters are critical for HSDM. This paper proposes a surface roughness prediction method based on Gaussian process regression (GPR), where cutting parameters and cutting forces are used as input variables. For 30CrMnSiNiA steel, HSDM experiments were carried out by considering spindle speed, feed per tooth, depth of cut, and width of cut as factors. Based on the experimental results, a comparison with support vector machine (SVM) and artificial neural network (ANN) is conducted to verify the effectiveness and superiority of the proposed prediction method with R2 of 0.9818 and 0.9736 on the training and test sets, respectively. In addition, considering the cutting parameters optimization, a GPR-based cutting force feature model is established, and its superiority is verified by comparing it with the traditional exponential model. Then, a multi-objective cutting parameters optimization model with surface roughness and material removal rate as the objectives is built by combining the surface roughness model with the cutting force feature model, and the optimal cutting parameters are solved by the multi-objective Harris hawks optimization (MOHHO) algorithm. Compared with the optimal results obtained from experiments, the use of cutting parameters with high speed, large feed per tooth, small depth of cut, and small width of cut is an effective strategy to balance machining quality and efficiency.

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References

  1. Zheng GM, Cheng X, Li L, Xu RF, Tian YB (2019) Experimental investigation of cutting force, surface roughness and tool wear in high-speed dry milling of AISI 4340 steel. J Mech Sci Technol 33(1):341–349. https://doi.org/10.1007/s12206-018-1236-z

    Article  Google Scholar 

  2. Khatir FA, Sadeghi MH, Akar S (2021) Investigation of surface integrity in the laser-assisted turning of AISI 4340 hardened steel. J Manuf Process 61:173–189. https://doi.org/10.1016/j.jmapro.2020.09.073

    Article  Google Scholar 

  3. Sugimoto K, Hojo T, Srivastava A (2019) Low and medium carbon advanced high-strength forging steels for automotive applications. Metals-Basel 9(12):1263. https://doi.org/10.3390/met9121263

    Article  Google Scholar 

  4. Kumar S, Singh D, Kalsi NS (2017) Analysis of surface roughness during machining of hardened AISI 4340 steel using minimum quantity lubrication. Mater Today Pro 4(2):3627–3635. https://doi.org/10.1016/j.matpr.2017.02.255

    Article  Google Scholar 

  5. Tian WW, Zhang J, Zhao F, Feng XB, Mei XS, Chen GD, Wang H (2022) Interpolation-based virtual sample generation for surface roughness prediction. J Intell Manuf. https://doi.org/10.1007/s10845-022-02054-4

  6. Lu XH, Hu XC, Wang H, Si LK, Liu YY, Gao LS (2016) Research on the prediction model of micro-milling surface roughness of Inconel718 based on SVM. Ind Lubr Tribol 68(2):206–211. https://doi.org/10.1108/ILT-06-2015-0079

    Article  Google Scholar 

  7. Cheng MH, Jiao L, Yan P, Li SY, Dai ZC, Qiu TY, Wang XB (2022) Prediction and evaluation of surface roughness with hybrid kernel extreme learning machine and monitored tool wear. J Manuf Process 84:1541–1556. https://doi.org/10.1016/j.jmapro.2022.10.072

    Article  Google Scholar 

  8. Pan YA, Kang RK, Dong ZG, Du WH, Yin S, Bao Y (2022) On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning. J Intell Manuf 33(3):675–685. https://doi.org/10.1007/s10845-020-01669-9

    Article  Google Scholar 

  9. Kaladhar M, Sahu G, Kumar SM, Nag BK, Aditya VS, Kaushik GS (2022) Evaluation and optimization of machinability issues in dry turning of DSS 2205. J Vib Eng Technol. https://doi.org/10.1007/s42417-022-00736-0

  10. Khawaja AH, Jahanzaib M, Cheema TA (2020) High-speed machining parametric optimization of 15CDV6 HSLA steel under minimum quantity and flood lubrication. Adv Prod Eng Manag 15(4):403–415. https://doi.org/10.14743/apem2020.4.374

    Article  Google Scholar 

  11. Imani L, Henzaki A, Hamzeloo R, Davoodi B (2020) Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA. Proc IMechEBJ EngManuf 234(5):920–932. https://doi.org/10.1177/0954405419889204

    Article  Google Scholar 

  12. Frifita W, Salem SB, Haddad A, Yallese MA (2020) Optimization of machining parameters in turning of Inconel 718 nickel-base super alloy. Mech Ind 21(2):203. https://doi.org/10.1051/meca/2020001

    Article  Google Scholar 

  13. Santhakumar J, Mohammed Iqbal U (2019) Parametric optimization of trochoidal step on surface roughness and dish angle in end milling of AISID3 steel using precise measurements. Materials 12(8):1335. https://doi.org/10.3390/ma12081335

    Article  Google Scholar 

  14. Hao ZP, Cheng G, Fan YH (2023) Research on surface roughness prediction in turning Inconel 718 based on Gaussian process regression. Phys Scr 98(1):15216. https://doi.org/10.1088/1402-4896/acaad5

    Article  Google Scholar 

  15. Zhao PY, Zhou M, Zhang YJ, Qiao GC (2018) Surface roughness prediction model in ultrasonic vibration assisted grinding of BK7 optical glass. J Cent South Univ 25(2):277–286. https://doi.org/10.1007/s11771-018-3736-5

    Article  Google Scholar 

  16. Lu J, Zhang ZK, Yuan XP, Ma JY, Hu SS, Xue B, Liao XP (2020) Effect of machining parameters on surface roughness for compacted graphite cast iron by analyzing covariance function of Gaussian process regression. Measurement 157:107578. https://doi.org/10.1016/j.measurement.2020.107578

    Article  Google Scholar 

  17. Yin R, Ke J, Mendis G, Sutherland JW (2019) A cutting parameter-based model for cost and carbon emission optimisation in a NC turning process. Int J Comput Integr Manuf 32(10):919–935. https://doi.org/10.1080/0951192X.2019.1667026

    Article  Google Scholar 

  18. Gopan V, Leo D, Evangeline G, Surendran A (2020) Experimental investigation for the multi-objective optimization of machining parameters on AISI D2 steel using particle swarm optimization coupled with artificial neural network. J Adv Manuf Syst 19(3):589–606. https://doi.org/10.1142/S0219686720500286

    Article  Google Scholar 

  19. Hsiao TC, Vu NC, Tsai MC, Dang XP, Huang SC (2021) Modeling and optimization of machining parameters in milling of Inconel-800 super alloy considering energy, productivity, and quality using nanoparticle suspended lubrication. Meas Control 54(5–6):880–894. https://doi.org/10.1177/0020294020925842

    Article  Google Scholar 

  20. Tian CL, Zhou GH, Zhang JJ, 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 

  21. Zainal N, Zain AM, Radzi NHM, Othman MR (2016) Glowworm swarm optimization (GSO) for optimization of machining parameters. J Intell Manuf 27(4):797–804. https://doi.org/10.1007/s10845-014-0914-7

    Article  Google Scholar 

  22. Wu TY, Lin CC (2021) Optimization of machining parameters in milling process of Inconel 718 under surface roughness constraints. Appl Sci 11(5):2137. https://doi.org/10.3390/app11052137

    Article  Google Scholar 

  23. Li YC, Zhou XQ, Lu MM, Lin JQ, Sun JB (2014) Parameters optimization for machining optical parts of difficult-to-cut materials by genetic algorithm. Mater Manuf Process 29(1):9–14. https://doi.org/10.1080/10426914.2013.852218

    Article  Google Scholar 

  24. Shin YC, Joo YS (1992) Optimization of machining conditions with practical constraints. Int J Prod Res 30(12):2907–2919. https://doi.org/10.1080/00207549208948198

    Article  Google Scholar 

  25. Su Y, Zhao GY, Zhao YG, Meng JB, Li CX (2020) Multi-objective optimization of cutting parameters in turning AISI 304 austenitic stainless steel. Metals-Basel 10(2):217. https://doi.org/10.3390/met10020217

    Article  Google Scholar 

  26. Chen XZ, Li CB, Tang 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 

  27. Jiang ZP, Gao D, Lu Y, Shang ZD, Kong LH (2020) Optimisation of cutting parameters for minimising carbon emissions and cost in the turning process. P I Mech Eng C-J Mec 236(4):1973–1985. https://doi.org/10.1177/0954406220922872

    Article  Google Scholar 

  28. Tian CL, Zhou GH, Lu Q, Zhang JJ, Xiao ZD, Wang R (2019) An integrated decision-making approach on cutting tools and cutting parameters for machining features considering carbon emissions. Int J Comput Integr Manuf 32(7):629–641. https://doi.org/10.1080/0951192X.2019.1610575

    Article  Google Scholar 

  29. Tian CL, Zhou GH, Lu FY, Chen ZH, Zou L (2020) An integrated multi-objective optimization approach to determine the optimal feature processing sequence and cutting parameters for carbon emissions savings of CNC machining. Int J Comput Integr Manuf 33(6):609–625. https://doi.org/10.1080/0951192X.2020.1775303

    Article  Google Scholar 

  30. Zhao LL, Fang YL, Lou P, Yan JW, Xiao AR (2021) Cutting parameter optimization for reducing carbon emissions using digital twin. Int J Precis Eng Manuf 22(5):933–949. https://doi.org/10.1007/s12541-021-00486-1

    Article  Google Scholar 

  31. Zhou GH, Zhang C, Lu FY, Zhang JJ (2020) Integrated optimization of cutting parameters and tool path for cavity milling considering carbon emissions. J Clean Prod 250:119454. https://doi.org/10.1016/j.jclepro.2019.119454

    Article  Google Scholar 

  32. Liang T, Chai CJ, Sun HX, Tan JX (2022) Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC. Energy 250:123761. https://doi.org/10.1016/j.energy.2022.123761

    Article  Google Scholar 

  33. Fu WL, Lu QP (2020) Multiobjective optimal control of FOPID controller for hydraulic turbine governing systems based on reinforced multiobjective Harris hawks optimization coupling with hybrid strategies. Complexity 2020:1–17. https://doi.org/10.1155/2020/9274980

    Article  Google Scholar 

  34. Gölcük L, Ozsoydan FB (2021) Quantum particles-enhanced multiple Harris hawks swarms for dynamic optimization problems. Expert Syst Appl 167:114202. https://doi.org/10.1016/j.eswa.2020.114202

    Article  Google Scholar 

  35. Zhao XK, Li CB, Chen XZ, Cui JB, Cao B (2022) Data-driven cutting parameters optimization method in multiple configurations machining process for energy consumption and production time saving. Int J of Precis Eng and Manuf-Green Tech 9(3):709–728. https://doi.org/10.1007/s40684-021-00373-0

    Article  Google Scholar 

  36. Hong XD, Ding YS, Ren LH, Chen L, Huang B (2018) A weighted heteroscedastic Gaussian process modelling via particle swarm optimization. Chemom Intell Lab Syst 172:129–138. https://doi.org/10.1016/j.chemolab.2017.11.019

    Article  Google Scholar 

  37. Zhang J, Kang XZ, Cao HJ, Yi H, Huang XF, Li CC, Tao GB (2023) Research on feasible region of specific cutting energy and surface roughness in high-speed dry milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts. Int J Adv Manuf Technol 125(1-2):133–155. https://doi.org/10.1007/s00170-022-10647-9

    Article  Google Scholar 

  38. Li B, Tian XT, Zhang M (2022) Modeling and multi-objective optimization method of machine tool energy consumption considering tool wear. Int J Precis Eng Manuf - Green Technol 9(1):127–141. https://doi.org/10.1007/s40684-021-00320-z

    Article  Google Scholar 

  39. Xian C, Shi YY, Luo J, Yang C (2021) Milling force modeling for disc milling cutter of indexable three-sided inserts considering tool runout. Int J Adv Manuf Technol 115(7-8):2191–2204. https://doi.org/10.1007/s00170-021-07133-z

    Article  Google Scholar 

  40. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  41. Cao WD, Ni JJ, Jiang BY, Ye CQ (2021) A three-stage parameter prediction approach for low-carbon gear hobbing. J Clean Prod 289:125777. https://doi.org/10.1016/j.jclepro.2020.125777

    Article  Google Scholar 

  42. Liu L, Qu D, Cao HJ, Huang XF, Song Y, Kang XZ (2022) Process optimization of high machining efficiency and low surface defects for HSD milling UD-CF/PEEK with limited thermal effect. J Manuf Process 76:532–547. https://doi.org/10.1016/j.jmapro.2022.02.040

    Article  Google Scholar 

  43. Gu CH, Liu XP, Luo F, Ding WC (2018) Improved NSGA-II algorithm based on synchronous update of external archive. Comput Appl Eng Educ 54(20):28–34

    Google Scholar 

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Funding

This work was supported by the National Key R&D Program of China (No. 2020YFB2010500).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, No. 174, Shazheng Street, Shapingba District, Chongqing, China

    Lei Song, Chunping Yan, Gan Tu, Minghong Xiang & Yifan Liu

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  1. Lei Song
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  2. Chunping Yan
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  3. Gan Tu
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  4. Minghong Xiang
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Contributions

Lei Song: conceptualization, investigation, methodology, software, validation, formal analysis, data curation, and writing - original draft; Chunping Yan: supervision, writing - review and editing, project administration, funding acquisition, and resources; Gan Tu: data curation and software; Minghong Xiang: data curation and software; and Yifan Liu: methodology

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Correspondence to Chunping Yan.

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Appendices

Appendix 1

see Table 13

Table 13 Statistical characteristics of cutting force in X direction
Full size table

see Table 14

Table 14 Statistical characteristics of cutting force in Y direction
Full size table

see Table 15

Table 15 Statistical characteristics of cutting force in Z direction
Full size table

Appendix 2

see Table 16

Table 16 Explanation of variables
Full size table

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Song, L., Yan, C., Tu, G. et al. Prediction and optimization of surface roughness in high-speed dry milling of 30CrMnSiNiA using GPR and MOHHO algorithm. Int J Adv Manuf Technol 128, 4357–4377 (2023). https://doi.org/10.1007/s00170-023-12167-6

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