摘要
在预测铸造缩孔、缩松缺陷时引入机器学习,采用具有针对性的数据预处理方式处理铸造工艺数据,同时采取多模态的数据输入方式增加数据维度,并使用全连接卷积神经网络对铸造缩孔、缩松缺陷进行快速计算。通过对某轴承座铸件进行分析,验证了该方法的有效性。相比于数值模拟方法,该方法具备较高的精度,同时可大幅缩短铸造缩孔、缩松缺陷的计算时间。
The machine learning method was applied to predict shrinkage defects in casting process.The targeted pre-processing method was employed to process casting data,and the multi-modal data input method was adopted to increase the data dimension.Fully convolutional networks were used to fast calculate casting shrinkage porosity defects.Based on the fast prediction method,a typical casting was selected for analysis,which verified the effectiveness of the method.Compared with the numerical simulation method,this method has high accuracy,and can greatly reduce the calculation time of casting shrinkage defeets.
作者
张建明
廖敦明
孙飞
Zhang Jianming;Liao Dunming;Sun Fei(State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology)
出处
《特种铸造及有色合金》
CAS
北大核心
2020年第8期841-845,共5页
Special Casting & Nonferrous Alloys
基金
广东省重点领域研发计划资助项目(2019B090921001)。
关键词
铸造缺陷
全连接卷积神经网络
深度学习
多模态数值模拟
Casting Defect
Fully Convolutional Networks
Deep Learning
Multi-modal Numerical Simulation
