基于小样本数据驱动的滚齿工艺参数低碳优化决策方法

doi:10.3969/j.issn.1004-132X.2022.13.011

摘要/Abstract

摘要： 针对实际生产历史数据不足的情况，提出一种基于小样本数据驱动的碳排放预测和多目标优化模型。通过Box-Behnken实验设计收集加工数据后，采用反向传播神经网络建立面向碳排放和加工效率的预测模型，在保证预测精度的同时有效减少模型对数据量的需求。以总碳耗和总时长为优化目标，采用改进的多目标灰狼算法和熵权-逼近理想解排序综合评价法进行了最优工艺参数决策。加工实验验证了提出方法的有效性。

关键词: 低碳优化, 小样本驱动, 改进灰狼优化算法, 熵权-逼近理想解排序综合评价法

Abstract: Aiming at the shortages of effective historical data in actual manufaction， a carbon emission prediction and multi-objective optimization model driven by small sample data was proposed. The Box-Behnken experimental design was used to collect processing data， and then the back propagation neural network was used to establish a prediction model for carbon emissions and processing efficiency， which ensured the prediction accuracy with less historical sample data. Aiming to optimize the total carbon consumption and makespan， the improved gray wolf optimization algorithm and entropy-TOPSIS comprehensive evaluation were used for determining the optimal processing parameters. Finally， the effectiveness of the proposed method was verified by machining experiments.

Key words: low carbon optimization, small sample drive, improved gray wolf optimization algorithm, entropy-（TOPSIS） comprehensive evaluation technology

中图分类号:

TH186

易茜, 柳淳 , 李聪波, 易树平, 何爽. 基于小样本数据驱动的滚齿工艺参数低碳优化决策方法[J]. 中国机械工程, 2022, 33(13): 1604-1612.

YI Qian, LIU Chun, LI Congbo, YI Shuping, HE Shuang. A Low Carbon Optimization Decision Method for Gear Hobbing Process Parameters Driven by Small Sample Data[J]. China Mechanical Engineering, 2022, 33(13): 1604-1612.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2022.13.011

https://www.cmemo.org.cn/CN/Y2022/V33/I13/1604

参考文献

［1］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.
LI Congbo， FU Song， CHEN Xingzhen， 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.
［2］CHEN Xingzhen， LI Congbo， TANG Ying， et al. Integrated Optimization of Cutting Tool and Cutting Parameters in Face Milling for Minimizing Energy Footprint and Production Time［J］. Energy， 2019， 175：1021-1037.
［3］倪恒欣，阎春平，陈建霖，等. 高速干切滚齿工艺参数的多目标优化与决策方法［J］. 中国机械工程， 2021， 32（7）：832-838.
NI Hengxin， YAN Chunping， CHEN Jianlin， et al. Multi-objective Optimization and Decision-making Method of High Speed Dry Gear Hobbing Process Parameters［J］. China Mechanical Engineering， 2021， 32（7）：832-838.
［4］吕景祥，唐任仲，郑军. 数据驱动的车削和钻削加工能耗预测［J］. 计算机集成制造系统， 2020， 26（8）：2073-2082.
LYU Jingxiang， TANG Renzhong， ZHEN Jun. Data-driven Methodology for Energy Consumption Prediction of Turning and Drilling Processes［J］. Computer Integrated Manufacturing Systems， 2020， 26（8）：2073-2082.
［5］XIAO Qinge， LI Congbo， TANG Ying， et al. Energy Efficiency Modeling for Configuration-dependent Machining via Machine Learning：a Comparative Study［J］. IEEE Transactions on Automation Science and Engineering， 2021， 18：717-730.
［6］CAO Weidong. ， YAN Chunping， 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， 93：4099-4110.
［7］SUN Shouli， WANG Shilong， WANG Yawen， et al. Prediction and Optimization of Hobbing Gear Geometric Deviations［J］. Mechanism and Machine Theory， 2018， 120：288-301.
［8］刘艺繁，阎春平，倪恒欣，等. 基于GABP和改进NSGA-Ⅱ的高速干切滚齿工艺参数多目标优化决策［J］. 中国机械工程， 2021， 32（9）：1043-1050.
LIU Yifan， YAN Chunping， NI Hengxin， et al. Multi-objective Optimization Decision of High-speed Dry Hobbing Process Parameters Based on GABP and Improved NSGA-Ⅱ［J］. China Mechanical Engineering， 2021， 32（9）：1043-1050.
［9］王立平，张兆坤，邵珠峰，等. 机床制造加工数字化车间信息模型及其应用研究［J］. 机械工程学报， 2019， 55（9）：154-165.
WANG Liping， ZHANG Zhaokun， SHAO Zhufeng， et al. Research on the Information Model of Digital Machining Workshop for Machine Tools and Its Applications［J］. Journal of Mechanical Engineering， 2019， 55（9）：154-165.
［10］CHAHAL H， TONER H， RAHKOVSKY I. Small Datas Big AI Potential［R/OL］. Washington D C：U. S. Center for Security and Emerging Technology， 2021.
［11］LINDSTROM M. Small Data：The Tiny Clues that Uncover Huge Trends［M］. London：St. Martins Press， 2016.
［12］AUGUSTIN N， FARAWAY J. When Small Data Beats Big Data［J］. Statistics & Probability Letters， 2018， 136：142-145.
［13］陈国青，张瑾，王聪，等. “大数据—小数据”问题：以小见大的洞察［J］. 管理世界， 2021， 37（2）：203-213.
CHEN Guoqing， ZHANG Jin， WANG Cong， et al. The “Big Data-Small Data” Problem：Insights for the Big through the Small［J］. Management World， 2021， 37（2）：203-213.
［14］LI H， WEN G， JIA X， et al. Augmenting Features by Relative Transformation for Small Data［J］. Knowledge-Based Systems， 2021， 225（7）：107121.
［15］FISHER O J， WATSON N J， ESCRIG J E， et al. Considerations， Challenges and Opportunities When Developing Data-driven Models for Process Manufacturing Systems［J］. Computers & Chemical Engineering， 2020， 140：106881.
［16］SINGH B， MISRA J. SurfaceFinish Analysis of Wire Electric Discharge Machined Specimens by RSM and ANN Modeling［J］. Measurement， 2019， 137：225-237.
［17］HAMID H A， JENIDI Y， THIELEMANS W， et al. Predicting the Capability of Carboxylated Cellulose Nanowhiskers for the Remediation of Copper from Water Using Response Surface Methodology（RSM）and Artificial Neural Network（ANN）Models［J］. Industrial Crops and Products， 2016， 93：108-120.
［18］JOSHI A G， SURESH R， MALLAIAH M. Experimental Investigation on Tool Wear in AISI H13 Die Steel Turning Using RSM and ANN Methods［J］. Arabian Journal for Science and Engineering， 2020， 46：2311-2325.
［19］生态环境部应对气候变化司. 2019年度减排项目中国区域电网基准线排放因子［EB/OL］. ［2020-12-29］. http：∥www. mee. gov. cn/ywgz/ydqhbh/wsqtkz/202012/t20201229_815386. shtml.
Department of Climate Change， Ministry of Ecology and Environment. Emission Factors of Chinas Regional Power Grid Base Line in 2019［EB/OL］. ［2020-12-29］. http：∥www. mee. gov. cn/ywgz/ydqhbh/wsqtkz/202012/t20201229_815386. shtml.
［20］李爱平，古志勇，朱璟，等. 基于低碳制造的多工步孔加工切削参数优化［J］. 计算机集成制造系统， 2015， 21（6）：1515-1522.
LI Aiping， GU Zhiyong， ZHU Jing， et al. Optimization of Cutting Parameters for Multi-pass Hole Machining Based on Low Carbon Manufacturing［J］. Computer Integrated Manufacturing Systems， 2015， 21（6）：1515-1522.
［21］RAJEMI M F， MATIVENGA P T， ARAMCHAROEN A. Sustainable Machining：Selection of Optimum Turning Condition Based on Minimum Energy Consideration［J］. Journal of Cleaner Production, 2010， 18：1059-1065.
［22］XIAO Qinge， LI Congbo， TANG Ying， et al. Multi-component Energy Modeling and Optimization for Sustainable Dry Gear Hobbing［J］. Energy， 2019， 187（2）：115911.
［23］曹永娟，冯亮亮，毛瑞，等. 轴向磁场永磁记忆电机多目标分层优化设计［J］. 中国电机工程学报， 2021， 41（6）：1983-1992.
CAO Yongjuan， FENG Liangliang， MAO Rui， et al. Multi-objective Stratified Optimization Design of Axial-flux Permanent Magnet Memory Motor［J］. Proceedings of the CSEE， 2021， 41（6）：1983-1992.
［24］MING Wuyi， HOU Junjian， ZHANG Zhen. et al. Integrated ANN-LWPA for Cutting Parameter Optimization in WEDM［J］. The International Journal of Advanced Manufacturing Technology， 2016， 84：1277-1294.
［25］姜春英，康玉祥，叶长龙，等. 改进PSO＿BP＿Adaboost算法在尺寸超差故障诊断中的应用［J］. 中国机械工程， 2018， 29（20）：2490-2494.
JIANG Chunying， KANG Yuxiang， YE Changlong， et al. Application of Improved PSO_BP_Adaboost Algorithm in Fault Diagnosis of Dimension Out-of-tolerance［J］. China Mechanical Engineering， 2018， 29（20）：2490-2494.
［26］FORESEE F D， HAGAN M T. Gauss-Newton Approximation to Bayesian Learning［C］∥Proceedings of International Conference on Neural Networks. Houston， 1997：1930-1935.
［27］易茜，何爽，宁轻，等. 汽车试制车间考虑员工作业能力的多目标优化生产调度［J］. 中国机械工程， 2021， 32（13）：1617-1629.
YI Qian， HE Shuang， NING Qing， et al. A Multi-objective Optimized Production Scheduling for Automobile Prototype Workshops Considering Employee Work Ability［J］. China Mechanical Engineering， 2021， 32（13）：1617-1629.
［28］DU Junliang， LIU Sifeng， LIU Yong. A Novel Grey Multi-criteria Three-way Decisions Model and Its Application［J］. Computers & Industrial Engineering， 2021， 158：107405.
［29］张宝珠，郭秀英. 齿轮加工速查手册［M］. 2版. 北京：机械工业出版社， 2017.
ZHANG Baozhu， GUO Xiuying. Gear Machining Quick Reference Manual ［M］. 2nd ed. Beijing：China Machine Press， 2017.