铸造2024，Vol.73Issue(9)：1329-1335,7.
铸造工艺数据驱动的工程机械铸件缺陷预测
Casting Process Data-Driven Defect Prediction for Construction Machinery Castings
摘要
Abstract
Directing at the problems of difficult to find defect cause,and category imbalance of technical data during sand casting process,a convolutional neural network defect prediction method based on feature redistribution and cost sensitive learning was proposed to solve the defects in sand casting process.Firstly,according to the feature correlation of samples,the sequence of feature vectors is optimized.Secondly,the cost sensitive regular term is designed based on the unbalanced process data sample,and the model loss function is modified.Finally,a defect prediction model(FR-CS-CNN)is constructed.The test results show that the overall prediction accuracy of FR-CS-CNN constructed in this study reaches 93.67%,which is 2.96%higher than that of convolutional neural network.关键词
砂型铸造/缺陷预测/代价敏感学习/卷积神经网络Key words
sand casting/defect prediction/cost-sensitive learning/convolutional neural networks分类
矿业与冶金引用本文复制引用
刘迎辉,周建新,余朋,潘徐政,朱守琴,计效园,吴来发,殷亚军,沈旭,解明国..铸造工艺数据驱动的工程机械铸件缺陷预测[J].铸造,2024,73(9):1329-1335,7.基金项目
国家重点研发计划项目(2020YFB1710100) （2020YFB1710100）
国家自然科学基金(52275337、52090042、51905188). （52275337、52090042、51905188）
