铸造技术2024，Vol.45Issue(2)：173-178,6.DOI:10.16410/j.issn1000-8365.2024.3179
基于数据合成与机器学习的6DM气缸体复杂铸件缺陷预测
Defect Prediction of 6DM Cylinder Block Complex Castings Based on Data Synthesis and Machine Learning
摘要
Abstract
The problems caused by defects in complex castings are particularly serious in automotive core part manufacturing and other key areas,which makes it urgent to predict the defects of complex castings and improve their production quality.In this paper,aiming at the problem of serious imbalance in the production data of complex 6DM cylinder block castings,such as those of pores and sand holes collected during the actual casting process,the defect prediction of complex 6DM cylinder block castings based on data synthesis and machine learning was studied,and the research status of artificial neural networks and complex casting defect prediction was combed.Combined with the on-site production situation of enterprises,demand analysis was carried out,and the production data of 6DM cylinder block complex castings were obtained.The synthetic dataset created based on the synthetic minority oversampling technique(SMOTE)algorithm was adopted as the dataset of the training model,which achieved a prediction accuracy of 99.37%.The results show that the constructed defect prediction model can accurately predict the defects in complex castings.关键词
6DM气缸体/缺陷预测/不平衡数据/数据合成/SMOTE算法Key words
6DM cylinder block/defect prediction/unbalanced data/data synthesis/SMOTE algorithm分类
矿业与冶金引用本文复制引用
王传胜,计效园,周建新,冯相灿,潘徐政,高峰,刘冰,李岩,韩宇,钟东彦,付煜..基于数据合成与机器学习的6DM气缸体复杂铸件缺陷预测[J].铸造技术,2024,45(2):173-178,6.基金项目
国家重点研发计划(2020YFB1710100) （2020YFB1710100）
国家自然科学基金(51905188,52090042,51775205) （51905188,52090042,51775205）
