铜业工程Issue(2)：18-22,5.DOI:10.3969/j.issn.1009-3842.2024.02.003
基于机器学习的Cu-Ni-Co-Si合金固溶处理晶粒尺寸预测
Prediction of Grain Size in Cu-Ni-Co-Si Alloy Solid Solution Treatment Based on Machine Learning
摘要
Abstract
The grain size of Cu-Ni-Co-Si alloy after solid solution treatment will affect its service performance.Three different machine learning methods,namely BP neural network,random forest,and long short-term memory network,were used to establish machine learning prediction models for the solid solution temperature and time of Cu-Ni-Co-Si alloy on the grain size after solid solution.The prediction accuracy of the three different machine learning models was compared,and it was found that the BP neural network model had the highest prediction accuracy,with an average relative error of 8.55%.Subsequently,genetic algorithm was used to optimize the BP neural network.The results showed that the average relative error of the established BP-GA model was reduced by 6.47%compared to the BP neural network model,with an average relative error of 2.08%,which can effectively guide the selection of process parameters for Cu-Ni-Co-Si alloy solid solution treatment.关键词
Cu-Ni-Co-Si合金/固溶处理/工艺参数/晶粒尺寸/机器学习Key words
Cu-Ni-Co-Si alloy/solution treatment/process parameters/grain size/machine learning分类
矿业与冶金引用本文复制引用
闫碧霄,朱雪彤,吴钢,陈慧琴..基于机器学习的Cu-Ni-Co-Si合金固溶处理晶粒尺寸预测[J].铜业工程,2024,(2):18-22,5.基金项目
中央引导地方科技发展资金自由探索类项目(YDZJSX2021A039) （YDZJSX2021A039）
山西省研究生优秀创新项目(2022Y667)资助 （2022Y667）
