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

Abstract:  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. 

Key words:  , process manufacturing, multi-process parameter optimization, improved particle swarm optimization(IPSO), long and short term memory(LSTM) neural network, extreme gradient boosting（XGBoost）