Product Quality Prediction with Deep Transfer Learning for Smart Factories

This paper proposes to use deep transfer learning (DTL) "layer freezing" method to build deep neural network (DNN) models for a target domain with few data on the basis of well-trained DNN models for a source domain with abundant data. Experiments using the DTL method are conducted for building DNN models to predict product surface roughness of wire electrical discharge machining (WEDM). The experimental results show that DTL indeed can help fast build models with high prediction accuracy for the target domain having few data.