用可解释AI优化注塑工艺，缺陷率降至0.13%。
Enhancing the Product Quality of the Injection Process Using eXplainable Artificial Intelligence
- 基于XGBoost和LightGBM预测产品是否缺陷
- 通过SHAP与ICE提取关键控制特征并确定最优范围
- 适合制造行业从业者及工业AI落地研究者
注塑成型是电子、汽车等行业将液态树脂固化成特定模具的重要制造技术，虽不直接构成发动机或芯片主体，但决定产品最终形态。近年来研究持续降低注塑缺陷率。本文提出一种基于可解释人工智能（XAI）的注塑过程控制优化系统，采用梯度提升算法（XGBoost与LightGBM）作为树模型分类器，预测产品是否为缺陷品。利用SHapley Additive exPlanations（SHAP）提取关键工艺特征，再通过个体条件期望（ICE）分析这些特征的最优控制区间。基于韩国人工智能制造平台（KAMP）提供的真实注塑制造数据集进行案例验证，结果表明：缺陷率从原始的1.00%分别降至使用XGBoost时的0.21%和LightGBM时的0.13%。
原文摘要 · Abstract (English)
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. Although the process is not related to creating the main part of engines or semiconductors, this manufacturing methodology sets the final form of the products. Re-cently, 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 XAI (eXplainable Artificial Intelligence) ap-proaches. Boosting algorithms (XGBoost and LightGBM) 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, 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 KAMP (Korea AI Manufacturing Platform) 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.
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