基于集成学习和改进粒子群优化算法的流程制造工艺参数优化  

Optimization of Process Parameters in Process Manufacturing Based on Ensemble Learning and Improved Particle Swarm Optimization Algorithm

作　　者：刘孝保[1] 严清秀 易斌 姚廷强[1] 顾文娟[1] LIU Xiaobao;YAN Qingxiu;YI Bin;YAO Tingqiang;GU Wenjuan(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming,650500;Technology Center,China Tobacco Yunnan Industrial Co.,Ltd.,Kunming,650500)

机构地区：[1]昆明理工大学机电工程学院,昆明650500 [2]云南中烟工业有限责任公司技术中心,昆明650500

出　　处：《中国机械工程》2023年第23期2842-2853,共12页China Mechanical Engineering

基　　金：云南省重大科技专项(202302AD080001)。

摘　　要：针对流程制造过程中工艺过程复杂、多工序耦合严重、工艺参数优化困难等问题,提出一种基于长短期记忆(LSTM)神经网络、极限梯度提升(XGBoost)算法和改进粒子群优化(IPSO)算法的多工序工艺参数融合优化方法。基于LSTM神经网络建立了数据预处理模型,通过LSTM神经网络提取流程工艺数据的时序特征,进而实现了对工艺数据中异常值的处理。在此基础上,通过XGBoost算法拟合工艺参数与质量指标间的非线性关系,并结合粒子群算法构建了PSO-XGBoost质量预测模型,再将预测模型的输出作为适应度,调用改进粒子群算法反向搜索全局最优工艺参数,得到各工序的最优工艺参数组合,从而实现了流程制造加工质量的融合优化。以某企业的一条流程生产线为例,验证了多工序工艺参数融合优化模型的有效性。Considering the complexity of technological processes,the serious coupling between multiple processes and the difficulties in optimizing process parameters during the process manufacturing,a multi-process technological parameter fusion optimization method was proposed based on LSTM neural network,XGBoost algorithm and IPSO algorithm.A data preprocessing model was established based on LSTM neural network,and the time series characteristics of processing data were extracted through LSTM neural network,which realized the processing of outlier in process data.And a PSO-XGBoost quality prediction model was constructed by fitting the nonlinear relationship between processing parameters and quality indexes with XGBoost and combining with particle swarm optimization algorithm.Then the output of the quality prediction model was taken as the fitness,and the improved particle swarm algorithm was used for trolling the global optimal processing parameters,which realized the fusion optimization of the quality of process manufacturing.A process production line of an enterprise was taken as an example to verify the effectiveness of the multi-process technological parameter fusion optimization model.

关 键 词：流程制造 多工序工艺参数优化 改进粒子群优化算法 长短期记忆神经网络 极限梯度提升 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]