用可解释AI精简传感器数据，提升注塑件质量分类效果
Improving Industrial Injection Molding Processes with Explainable AI for Quality Classification
- 通过SHAP等XAI技术筛选关键特征，从19维降至6维
- 特征减少后模型准确率不变，推理速度小幅提升
- 适合传感器少的工业场景，提升AI落地可行性
机器学习是优化工业质量控制的关键工具，但模型复杂性常因缺乏可解释性而限制实际应用。此外，许多工业设备传感器不全，导致数据采集不完整。可解释人工智能（XAI）通过揭示模型决策依据并识别关键特征，提供了解决方案。本文研究基于XAI的特征降维对注塑件质量分类的影响。我们在一个基于真实生产数据训练的长短期记忆模型上，应用SHAP、Grad-CAM和LIME分析特征重要性。将原始19个输入特征缩减至9个和6个，评估了模型精度、推理速度与可解释性之间的权衡。结果表明，特征减少可在保持高分类性能的同时提升泛化能力，并带来轻微的推理速度提升。该方法增强了受限传感器条件下的AI质量控制可行性，为制造领域更高效、可解释的机器学习应用铺平道路。
原文摘要 · Abstract (English)
Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.
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