Casting Process Data-Driven Defect Prediction for Construction Machinery Castings
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.
国家科技期刊平台
NSTL
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