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

作　　者：王传胜 冯相灿 潘徐政 高峰 刘冰 李岩 韩宇 钟东彦 付煜 计效园[2] 周建新[2] WANG Chuansheng;FENG Xiangcan;PAN Xuzheng;GAO Feng;LIU Bing;LI Yan;HAN Yu;ZHONG Dongyan;FU Yu;JI Xiaoyuan;ZHOU Jianxin(FAW Foundry Co.,Ltd,Changchun 130062,China;State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]一汽铸造有限公司,吉林长春130062 [2]华中科技大学材料成形与模具技术国家重点实验室,湖北武汉430074

出　　处：《铸造技术》2024年第2期173-178,共6页Foundry Technology

基　　金：国家重点研发计划(2020YFB1710100);国家自然科学基金(51905188,52090042,51775205)。

摘　　要：在汽车核心零部件制造等关键领域,复杂铸件出现缺陷的后果尤为严重,因此对复杂铸件进行缺陷预测并提高其生产质量刻不容缓。本文针对实际铸造过程中采集到的6DM气缸体复杂铸件生产数据中气孔、砂眼等缺陷类别的数据量严重不平衡问题,对基于数据合成与机器学习的6DM气缸体复杂铸件缺陷预测进行研究,梳理了人工神经网络与复杂铸件缺陷预测的研究现状,结合企业现场生产情况,开展了需求分析,获取6DM气缸体复杂铸件生产数据。并基于SMOTE(synthetic minority oversampling technique)算法,创建了合成数据集,采用合成数据集作为训练模型的数据集,预测准确率达到99.37%。结果表明,构建的复杂铸件缺陷预测模型能够准确预测复杂铸件缺陷。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 

分 类 号：TG245[金属学及工艺—铸造] TP181[自动化与计算机技术—控制理论与控制工程]