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.