Abstract
Plastic injection molding is a widely used polymer-processing method. As the requirements for processing accuracy have become increasingly stringent, defect analysis in plastic injection molding is necessary to improve the product yield. Causal discovery has recently gained attention for defect analysis in many processes. Because injection molding is a spatial process involving the distribution of physical quantities, spatial autocorrelation should be considered. Although the linear non-Gaussian acyclic model (LiNGAM) is a well-known causal discovery method, it cannot properly model spatial autocorrelation. In this study, a new causal discovery method for a spatially autocorrelated dependent variable, referred to as the Causal Structure Search for the Spatial Autoregressive Model (CASSPAR), is proposed. It models the causal relationships among the observed points without prior knowledge of the spatial structure. The proposed method represents the causal relationships among the observed points as a causal graph and estimates the adjacency matrix of the graph. The adjacency matrix is estimated using LiNGAM, and the model parameters are estimated using two-stage least squares (2SLS). The usefulness of the proposed CASSPAR was demonstrated using simulation data of a plastic injection molding process to identify the root cause of warpage.