基于小样本数据驱动的滚齿工艺参数低碳优化决策方法  

A Low Carbon Optimization Decision Method for Gear Hobbing Process Parameters Driven by Small Sample Data

作　　者：易茜[1,2] 柳淳 李聪波[1,2] 易树平[2] 何爽 YI Qian;LIU Chun;LI Congbo;YI Shuping;HE Shuang(State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing,400044;College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing,400044)

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400044 [2]重庆大学机械与运载工程学院,重庆400044

出　　处：《中国机械工程》2022年第13期1604-1612,共9页China Mechanical Engineering

基　　金：国家自然科学基金(52005062);国家重点研发计划(2018YFB1701205)。

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。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.

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 

分 类 号：TH186[机械工程—机械制造及自动化]