Enhancing the Product Quality of the Injection Process Using eXplainable Artificial Intelligence

Abstract

The injection molding process is a traditional technique for making products in various industries such as electronics and automobiles via solidifying liquid resin into certain molds. Recently, research has continued to reduce the defect rate of the injection molding process. This study proposes an optimal injection molding process control system to reduce the defect rate of injection molding products with eXplainable Artificial Intelligence (XAI) approaches. Boosting algorithms (XGBoost version 2.1.3 and LightGBM version 4.1.0) are used as tree-based classifiers for predicting whether each product is normal or defective. The main features to control the process for improving the product are extracted by Shapley Additive exPlanations (SHAP), while the individual conditional expectation analyzes the optimal control range of these extracted features. To validate the methodology presented in this work, the actual injection molding AI manufacturing dataset provided by the Korea AI Manufacturing Platform (KAMP) is employed for the case study. The results reveal that the defect rate decreases from 1.00% (original defect rate) to 0.21% with XGBoost and 0.13% with LightGBM, respectively.