Carbon Consumption Prediction Method of Gear Hobbing Based on Data Migration in Shared Manufacturing

doi:10.3969/j.issn.1004-132X.2023.15.007

Abstract

Abstract: Aiming at the problems that general manufacturing enterprises were not intelligent enough and it was difficult for a single enterprise to collect enough processing data， a prediction method of carbon consumption in gear hobbing processes was proposed based on data transfer between enterprises under shared manufacturing. The data characteristics of gear hobbing processes were analyzed， and the data migration method of improved TrAdaBoost algorithm was proposed to integrate the carbon consumption data of gear hobbing processes among enterprises under the data sharing manufacturing to form a cross-enterprise joint data set. The Dragonfly algorithm was used to optimize the support vector regression to construct a cross-enterprise carbon prediction model for gear hobbing processes. The effectiveness of the proposed method was verified by case analysis. Predictive performance has advantages when the data volume is small and the data correlation is low. Compared with the traditional algorithm， mean absolute percentage errors and coefficient of determination are improved by 59.23% and 16.56% respectively.

Key words: shared manufacturing, gear hobbing, transfer learning, carbon consumption prediction

摘要： 针对一般制造企业生产智能化程度不高、单个企业难以收集足够加工数据的问题，提出了一种共享制造下企业间数据迁移的滚齿加工碳耗预测方法。分析滚齿加工数据特征，在共享制造环境下提出用改进TrAdaBoost算法的数据迁移方法融合企业间的滚齿加工碳耗数据，形成跨企业联合数据集；利用蜻蜓算法优化支持向量回归，构造了跨企业滚齿加工碳耗预测模型。通过案例分析验证了所提方法的有效性，其预测性能在数据量少、数据关联性差的情况下具有优势，平均相对误差和决定系数分别比传统算法平均提高59.23%和16.56%。

关键词: 共享制造, 滚齿加工, 迁移学习, 碳耗预测

CLC Number:

TH186

YI Qian, LUO Yusong, HU Chunhui, ZHUO Junkang, LI Congbo, YI Shuping. Carbon Consumption Prediction Method of Gear Hobbing Based on Data Migration in Shared Manufacturing[J]. China Mechanical Engineering, 2023, 34(15): 1820-1831.

易茜, 罗雨松, 胡春晖, 卓俊康, 李聪波, 易树平. 共享制造下基于数据迁移的滚齿加工碳耗预测方法[J]. 中国机械工程, 2023, 34(15): 1820-1831.

References

［1］曹华军，李洪丞，曾丹，等. 绿色制造研究现状及未来发展策略［J］. 中国机械工程， 2020， 31（2）：135-144.
CAO Huajun， LI Hongcheng， ZENG Dan， et al. The State-of-art and Future Development Strategies of Green Manufacturing［J］. China Mechanical Engineering， 2020， 31（2）：135-144.
［2］中共中央国务院关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见［N］. 人民日报， 2021-10-25（001）.
Opinions of the CPC Central Committee and The State Council on Fully， Accurately and Comprehensively Implementing the New Development Concept to Achieve Carbon Peak and Carbon Neutrality［N］. Peoples Daily， 2021-10-25（001）.
［3］国家统计局. 中华人民共和国2020年国民经济和社会发展统计公报［J］. 中国统计， 2021（3）：8-22.
National Bureau of Statistics. Statistical Bulletin of the Peoples Republic of China on National Economic and Social Development in 2020［J］. China Statistics， 2021（3）：8-22.
［4］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 Processing Parameters［J］. China Mechanical Engineering， 2021， 32（7）：832-838.
［5］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.
［6］PRIARONE P C， ROBIGLIO M， SETTINERI L， et al. Modelling of Specific Energy Requirements in Machining as a Function of Tool and Lubricoolant Usage［J］. CIRP Annals—Manufacturing Technology， 2016， 65（1）：25-28.
［7］ALBERTELLI P， KESHARI A， MATTA A. Energy Oriented Multi Cutting Parameter Optimization in Face Milling［J］. Journal of Cleaner Production， 2016， 137：1602-1618.
［8］李聪波，尹誉先，肖溱鸽，等. 数据驱动下基于元动作的数控车削能耗预测方法［J］. 中国机械工程， 2020， 31（21）：2601-2611.
LI Congbo， YIN Yuxian， XIAO Qinge， et al. Data-driven Energy Consumption Prediction Method of CNC Turning Based on Meta-action［J］. China Mechanical Engineering， 2020， 31（21）：2601-2611.
［9］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.
［10］CAO Weidong， NI Jianjun， JIANG Boyan， et al. A Three-stage Parameter Prediction Approach for Low Carbon Gear Hobbing［J］. Journal of Cleaner Production， 2021， 289：125777.
［11］BHINGE R， PARK J， LAW K H， et al. Toward a Generalized Energy Prediction Model for Machine Tools［J］. Journal of Manufacturing Science and Engineering， 2017， 139（4）：041013.
［12］NGUYEN T T. Prediction and Optimization of Machining Energy， Surface Roughness， and Production Rate in SKD61 Milling［J］. Measurement， 2019， 136：525-544.
［13］ZHUANG Fuzhen， QI Zhiyuan， DUAN Keyu， et al. A Comprehensive Survey on Transfer Learning［J］. Proceedings of the IEEE， 2021， 109（1）：43-76.
［14］RIBEIRO M， GROLINGER K， ELYAMANY HF， et al. Transfer Learning with Seasonal and Trend Adjustment for Cross-building Energy Forecasting［J］. Energy and Buildings， 2018， 165：352-363.
［15］WU Zhenghong， JIANG Hongkai， ZHAO Ke， et al. An Adaptive Deep Transfer Learning Method for Bearing Fault Diagnosis［J］. Measurement， 2020， 151：107227.
［16］BRANDT E. Deborah Wince-Smith：a Vision for Shared Manufacturing［J］. Mechanical Engineering， 1990， 112（12）：52-55.
［17］晏鹏宇，杨柳，车阿大. 共享制造平台供需匹配与调度研究综述［J］. 系统工程理论与实践， 2022， 42（3）：811-832.
YAN Pengyu， YANG Liu， CHE Ada. Review of Supply-demand Matching and Scheduling in Shared Manufacturing［J］. Systems Engineering—Theory & Practice， 2022， 42（3）：811-832.
［18］臧冀原，刘宇飞，王柏村，等. 面向2035的智能制造技术预见和路线图研究［J］. 机械工程学报， 2022， 58（4）：285-308.
ZANG Jiyuan， LIU Yufei， WANG Baicun， et al. Technology Forecasting and Roadmapping of Intelligent Manufacturing by 2035［J］. Journal of Mechanical Engineering， 2022， 58（4）：285-308.
［19］易茜，柳淳，李聪波，等. 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， et al. 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.
［20］李均亮，王时龙，王四宝，等. 基于工件材料去除率的滚齿机床能耗模型［J］. 中国机械工程， 2020， 31（21）：2626-2631.
LI Junliang， WANG Shilong， WANG Sibao， et al. Energy Consumption Model of Hobbing Machine Tools Based on Workpiece Material Removal Rate［J］. China Mechanical Engineering， 2020， 31（21）：2626-2631.
［21］倪恒欣，阎春平，孙菡，等. 高速干切滚齿机床能耗分布特性及其预测模型［J］. 中国机械工程， 2022， 33（7）：842-851.
NI Hengxin， YAN Chunping， SUN Han， et al. Energy Consumption Distribution Characteristics and Prediction Model of High-speed Dry Gear Hobbing Machine［J］. China Mechanical Engineering， 2022， 33（7）：842-851.
［22］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 Xingzheng， 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.
［23］中国机械工程学会. 中国机械工程技术路线图（2021）［M］. 北京：机械工业出版社， 2022：367-371.
Chinese Mechanical Engineering Society. Technology Roadmaps of Chinese Mechanical Engineering（2021）［M］. Beijing：Mechanical Industry Press， 2022：367-371.
［24］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线排放因子［EB/OL］. ［2022-11-22］. 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］. ［2022-11-22］. http：∥www. mee. gov. cn/ywgz/ydqhbh/wsqtkz/202012/t20201229_815386. shtml.
［25］SCHUDELEIT T， ZST S， WEISS L， et al. The Total Energy Efficiency Index for Machine Tools［J］. Energy， 2016， 102：682-693.
［26］张雪松，庄严，闫飞，等. 基于迁移学习的类别级物体识别与检测研究与进展［J］. 自动化学报， 2019， 45（7）：1224-1243.
ZHANG Xuesong， ZHUANG Yan， YAN Fei， et al. Status and Development of Transfer Learning Based Category-level Object Recognition and Detection［J］. Acta Automatica Sinica， 2019， 45（7）：1224-1243.
［27］DAI Wenyuan， YANG Qiang， XUE Guirong， et al. Boosting for Transfer Learning［C］∥International Conference on Machine Learning. New York， 2007：1224-1243.
［28］VAPNIK V N. An Overview of Statistical Learning Theory［J］. IEEE Transactions on Neural Networks， 1999， 10（5）：988-999.
［29］赵京，李立明，尚红，等. 基于主成分分析法的机械臂运动灵活性性能综合评价［J］. 机械工程学报， 2014， 50（13）：9-15.
ZHAO Jing， LI Liming， SHANG Hong， et al. Comprehensive Evaluation of Robotic Kinematic Dexterity Performance Based on Principal Component Analysis［J］. Journal of Mechanical Engineering， 2014， 50（13）：9-15.
［30］李聪波，孙鑫，侯晓博，等. 数字孪生驱动的数控铣削刀具磨损在线监测方法［J］. 中国机械工程， 2022， 33（1）：78-87.
LI Congbo， SUN Xin， HOU Xiaobo， et al. Online Monitoring Method for NC Milling Tool Wear by Digital Twin-driven［J］. China Mechanical Engineering， 2022， 33（1）：78-87.
［31］陈果，周伽. 小样本数据的支持向量机回归模型参数及预测区间研究［J］. 计量学报， 2008， 29（1）：92-96.
CHEN Guo， ZHOU Jia. Research on Parameters and Forecasting Intreval of Support Vector Regression Model to Small Sample［J］. Acta Metrologica Sinica， 2008， 29（1）：92-96.
［32］MIRJALILI S. Dragonfly Algorithm：a New Meta-heuristic Optimization Technique for Solving Single-objective， Discrete， and Multi-objective Problems［J］. Neural Computing & Applications， 2016， 27：1053-1073.

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[13]	LI Junliang;WANG Shilong;WANG Sibao;XIA Changjiu. Energy Consumption Model of Hobbing Machine Tools Based on Workpiece Material Removal Rate [J]. China Mechanical Engineering, 2020, 31(21): 2626-2631.
[14]	LI Benjie;CAO Huajun;YANG Xiao;ZHOU Jin. Contribution of Chip Deformations and Crushes to Tool Failures in High-speed Dry Gear Hobbing [J]. China Mechanical Engineering, 2019, 30(01): 71-78.
[15]	Yuan Bin, Han Jiang, Wu Lulu, Tian Xiaoqing, Xia Lian. A Prediction Model and Experimental Study of Gear Hobbing Profile Errors Based on Particle Swarm Optimization [J]. China Mechanical Engineering, 2016, 27(20): 2705-2711.