Soft sensing technique for mass customization based on heterogeneous causal graph attention networks

Abstract

The soft sensing technique for mass customization (MC) process is always challenging due to its modular production, customized assembly and multi-scale. This paper proposes a soft sensing technique for MC process based on a heterogeneous causal graph attention network (HCGAT). Firstly, a priori causal additive model (PCAM) is proposed, which utilizes the mechanism and data to construct a causal skeleton jointly. On this basis, an additive noise model (ANM) together with scoring mechanism is designed to obtain the reliable causal graph. Secondly, self-learned weight parameter matrices are applied to the data block in each scale, enabling the mapping of distinct dimensional information onto a common dimension. Finally, a unique quality prediction framework is carried out to tackle the soft sensing modeling in MC process where specific combinations of different accessories are encoded for product classification. The performance of the proposed method is evaluated on a numerical simulation dataset and an electric automobile manufacturing dataset where the experimental results show the superiority of the methods in the efficacy and accuracy of quality prediction.