A multisource domain adaptation method for quality prediction in small-batch production systems

Quality prediction for small-batch production processes is a complex problem due to limitations in available training samples. In this study, a multisource domain adaptation joint-Y partial least square (PLS) method is proposed to learn the similarities between domains and use them to construct a quality prediction model. Without constraints on the number of source and target domains, the proposed method can transfer more historical information for the in-operation process than traditional methods. Numerical experiments and a real-world case study of quality prediction in computer wafer production are performed to verify the effectiveness of the proposed method. The results show that the prediction accuracy of the proposed method is high in cases with few training samples in the target domain compared to the accuracies of the joint-Y PLS model and the traditional PLS model.