Multi-objective Optimization of Processing Parameters and Decision-making Method in High-speed Dry Hobbing
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摘要: 高速干切滚齿过程无切削液且切削速度高，不合适的工艺参数将严重影响加工能耗、质量以及刀具寿命等。针对多目标滚齿工艺参数优化问题，构建了以最低能耗、最小质量误差以及最大刀具寿命为目标的多目标优化模型，提出了一种基于多目标遗传算法(NSGA-Ⅲ)以及层次分析法与优劣解距离排序法相结合(AHP-TOPSIS)的优化决策方法。该方法采用基于参考点的NSGA-Ⅲ算法寻优，利用AHP-TOPSIS组合方法进行各评价指标权重的动态计算，完成工艺参数解集优劣性能排序，便于更加高效合理的进行优化决策。
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关键词:
- 高速干切 /
- 工艺参数 /
- NSGA-Ⅲ算法 /
- 多目标优化 /
- AHP-TOPSIS
Abstract: The high-speed dry hobbing process has no cutting fluid and high cutting speed. Inappropriate process parameters will seriously affect processing energy consumption, quality and tool life. In order to solve the multi-objective optimization of processing parameter in hobbing, a multi-objective optimization model with minimum energy consumption, minimum mass error and maximum tool life as the goals is constructed, and a multi-objective genetic algorithm (NSGA-Ⅲ) and analytic hierarchy process are proposed. An optimized decision-making method combined with the superior and inferior solution distance ranking method (AHP-TOPSIS). The NSGA-Ⅲ algorithm based on reference points to optimize is used, and the AHP-TOPSIS combination method to dynamically calculate the weight of each evaluation index is done, the ranking of the pros and cons of the processing parameter solution set is completed, and which facilitates more efficient and reasonable optimization decisions. -
表 1 机床性能参数
型号 nz/(r·min-1) Fx/(mm·min-1) Fz/(mm·min-1) 最大加工模数/mm 切向形程/mm 最大加工直径/mm YDZ3126CNC 0~2 500 0~7 500 0~7 500 6 0~200 260 
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表 4 模型计算能耗中相关参数值
Pstandby/W Passist/W Tstandby/min a1 a2 b1 b2 c1=c2 2 300 245 3.5 2.13×10-6 1.33×10-5 -0.08 0.035 0
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表 6 滚切力和刀具寿命相关系数
cf uf vf xf yf zf k1 k2 k3 Cv Kv xv yv mv 18.2 0.27 0.28 1.76 0.65 0.82 1 1.08 1.11 289 0.68 0 0.5 0.33
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表 7 NSGA-Ⅲ求解的工艺参数集
解集序号Ji 决策变量 优化目标 fz/(mm·r-1) nz/(r·min-1) da0/mm z2取整后 Qerror/mm Etotal/J Ltool/min J1 1.551 6 1 300 90 3 0.028 6 6 632 490 330.341 5 J2 1.351 0 1 300 90 3 0.022 9 7 148 332 330.189 2 J3 1.281 4 1 265.4 84 2 0.020 5 7 769 636 330.150 8 J4 1.449 7 1 300 89 3 0.024 5 6 941 909 330.188 4 J5 1.298 8 1 267.2 98 3 0.021 7 7 588 220 330.210 3 J30 1.391 8 1 276.3 89 3 0.023 7 7 220 303 330.150 3
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表 8 优劣排序结果
对应解集号 相对接近度 排名 J2 0.728 39 1 J21 0.707 31 2 J10 0.704 36 3 J4 0.697 39 4 J25 0.696 06 5 J29 0.691 19 6 J9 0.676 58 7 J30 0.668 15 8 J12 0.667 74 9 J13 0.654 57 10 J8 0.649 59 11 J15 0.646 81 12 J6 0.641 36 13 J7 0.640 73 14 J11 0.635 62 15
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表 9 结果对比
名称 fz/(mm·r-1) nz/(r·min-1) da2/mm z2(取整) Etotal/J Qerror/mm Ltool/min 本文方法 1.351 0 1 300 90 3 7 148 332 0.022 9 330.189 2 NSGA-Ⅱ 1.381 3 1 293.7 89 3 7 267 248 0.024 0 330.102 MOPSO 1.634 8 1 289 89 2 7 462 018 0.024 5 330.118 相比NSGA-Ⅱ - - - - 1.64% 4.58% 0.026% 相比MOPSO - - - - 4.2% 6.53% 0.022%
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[1] TU G Z, WU S H, LIU J, et al. Cutting performance and wear mechanisms of Sialon ceramic cutting tools at high speed dry turning of gray cast iron[J]. International Journal of Refractory Metals and Hard Materials, 2016, 54: 330-334 doi: 10.1016/j.ijrmhm.2015.08.007 [2] SUN S L, WANG S L, WANG Y W, et al. Prediction and optimization of hobbing gear geometric deviations[J]. Mechanism and Machine Theory, 2018, 120: 288-301 doi: 10.1016/j.mechmachtheory.2017.09.002 [3] KANE M M, IVANOV B V, ZAGORSKAYA N B. Optimizing the hobbing of cylindrical gears[J]. Russian Engineering Research, 2014, 34(8): 526-529 doi: 10.3103/S1068798X14080061 [4] KARPUSCHEWSKI B, BEUTNER M, KÖCHIG M, et al. Cemented carbide tools in high speed gear hobbing applications[J]. CIRP Annals, 2017, 66(1): 117-120 doi: 10.1016/j.cirp.2017.04.016 [5] LIU X, ZHAO F, MEI X S. A fuzzy adaptive controller for constant cutting torque in high-performance gear hobbing process[C]//Proceedings of 2017 IEEE International Conference on Advanced Intelligent Mechatronics. Munich: IEEE, 2017: 1725-1730 [6] KLOCKE F, LÖPENHAUS C, SARI D. Process concepts for gear finish hobbing[J]. Procedia CIRP, 2016, 41: 875-880 doi: 10.1016/j.procir.2015.12.099 [7] 刘志, 卢红, 夏亮, 等. 行星齿轮高速干式滚切工艺参数优化设计[J]. 机械设计与制造, 2020(8): 239-242 doi: 10.3969/j.issn.1001-3997.2020.08.056LIU Z, LU H, XIA L, et al. CNC high-speed dry hobbing parameters optimization design of planetary gear[J]. Machinery Design & Manufacture, 2020(8): 239-242 (in Chinese) doi: 10.3969/j.issn.1001-3997.2020.08.056 [8] 钟健, 阎春平, 曹卫东, 等. 基于BP神经网络和FPA的高速干切滚齿工艺参数低碳优化决策[J]. 工程设计学报, 2017, 24(4): 449-458 doi: 10.3785/j.issn.1006-754X.2017.04.012ZHONG J, YAN C P, CAO W D, et al. Low carbon optimization decision for high-speed dry hobbing process parameters based on BP neural networks and FPA[J]. Chinese Journal of Engineering Design, 2017, 24(4): 449-458 (in Chinese) doi: 10.3785/j.issn.1006-754X.2017.04.012 [9] 李聪波, 付松, 陈行政, 等. 面向高效节能的数控滚齿加工参数多目标优化模型[J]. 计算机集成制造系统, 2020, 26(3): 676-687 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ202003011.htmLI C B, FU S, CHEN X Z, et al. Multi-objective CNC gear hobbing parameters optimization model for high efficiency and energy saving[J]. Computer Integrated Manufacturing Systems, 2020, 26(3): 676-687 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJJ202003011.htm [10] CAO W D, YAN C P, WU D J, et al. A novel multi-objective optimization approach of machining parameters with small sample problem in gear hobbing[J]. The International Journal of Advanced Manufacturing Technology, 2017, 93(9): 4099-4110 [11] 张应, 曹华军, 朱利斌, 等. 齿轮高速干切滚刀寿命预估模型与优化方法[J]. 中国机械工程, 2017, 28(21): 2614-2620 doi: 10.3969/j.issn.1004-132X.2017.21.017ZHANG Y, CAO H J, ZHU L B, et al. High-speed dry gear hob life prediction model and optimization method[J]. China Mechanical Engineering, 2017, 28(21): 2614-2620 (in Chinese) doi: 10.3969/j.issn.1004-132X.2017.21.017 [12] SARI D, TROβ N, LÖPENHAUS C, et al. Development of an application-oriented tool life equation for dry gear finish hobbing[J]. Wear, 2019, 426-427: 1563-1572 doi: 10.1016/j.wear.2018.12.037 [13] 陈鹏, 曹华军, 张应, 等. 齿轮高速干式滚切工艺参数优化模型及应用系统开发[J]. 机械工程学报, 2017, 53(1): 190-197 https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201701023.htmCHEN P, CAO H J, ZHANG Y, et al. The process parameters optimization model of gear high-speed dry hobbing and its application system development[J]. Journal of Mechanical Engineering, 2017, 53(1): 190-197 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB201701023.htm [14] 艾兴, 肖诗纲. 切削用量简明手册[M]. 3版. 北京: 机械工业出版社, 1994AI X, XIAO S G. A concise manual of machining parameter[M]. 3rd ed. Beijing: China Machine Press, 1994 (in Chinese) [15] DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601 doi: 10.1109/TEVC.2013.2281535 [16] 蒋浩鹏, 孙建诚, 杨文伟, 等. 基于AHP与改进TOPSIS权值算法的高速公路施工方案风险评价[J]. 武汉大学学报(工学版), 2020, 53(8): 698-703 https://www.cnki.com.cn/Article/CJFDTOTAL-WSDD202008007.htmJIANG H P, SUN J C, YANG W W, et al. Risk evaluation of highway construction plan based on AHP and improved TOPSIS weight algorithm[J]. Engineering Journal of Wuhan University, 2020, 53(8): 698-703 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WSDD202008007.htm [17] LI C B, LI L L, TANG Y, ZHU Y, et al. A comprehensive approach to parameters optimization of energy-aware CNC milling[J]. Journal of Intelligent Manufacturing, 2019, 30(1): 123-138 doi: 10.1007/s10845-016-1233-y [18] ZHANG J, CHO H J, MAGO P J, et al. Multi-Objective Particle Swarm Optimization (MOPSO) for a distributed energy system integrated with energy storage[J]. Journal of Thermal Science, 2019, 28(6): 1221-1235 doi: 10.1007/s11630-019-1133-5 -
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