Real-time monitoring solutions have gained popularity across industries due to the advent of Industry 4.0, AI, and big data enhancing the efficiency of industrial production and equipment decisions. Machine learning models that possess computing intelligence and interpretability provide superior predictive capabilities compared to manual adjustments, resulting in cost savings and manufacturing high-quality products. This study proposes a zero-defect manufacturing decision support system based on computational intelligence feature selection combined with interpretable machine learning. The decision support system integrates Particle Swarm Optimization (PSO) and the C4.5 decision tree method, abbreviated as PSO+C4.5, to enable the continuous monitoring of the injection molding process in real-time, considering production parameter information and collected data quality, guiding the decision-making process for implementing zero-defect manufacturing (ZDM). In contrast to existing research, our innovative methodology relies on computational intelligence techniques for extracting features and employs interpretable machine learning prediction models. In terms of quality prediction, our empirical findings show that the suggested method accomplishes the optimal balance between interpretability and predictive performance (Accuracy: 0.9889, Sensitivity: 0.9869, and Specificity: 0.9935). These characteristics can directly support maintenance personnel and operators in optimizing the processing quality process.

中文翻译：

---

基于计算智能和机器学习的新型决策支持系统：迈向注塑成型的零缺陷制造

由于工业4.0、人工智能和大数据的出现提高了工业生产和设备决策的效率，实时监控解决方案在各行业中受到欢迎。与手动调整相比，具有计算智能和可解释性的机器学习模型可提供卓越的预测能力，从而节省成本并制造高质量的产品。本研究提出了一种基于计算智能特征选择与可解释机器学习相结合的零缺陷制造决策支持系统。决策支持系统集成了粒子群优化（PSO）和C4.5决策树方法，简称PSO+C4.5，能够实时连续监控注塑过程，考虑生产参数信息和收集的数据质量，指导实施零缺陷制造（ZDM）的决策过程。与现有研究相反，我们的创新方法依赖于计算智能技术来提取特征并采用可解释的机器学习预测模型。在质量预测方面，我们的实证结果表明，建议的方法实现了可解释性和预测性能之间的最佳平衡（准确度：0.9889，灵敏度：0.9869，特异性：0.9935）。这些特性可以直接支持维护人员和操作人员优化加工质量流程。