Abstract
This study presents an applied integration of machine learning (ML) within the Process Monitoring for Quality (PMQ) framework to address persistent limitations in traditional quality control systems, particularly their inability to manage high-dimensional and real-time manufacturing data. This research enhances the PMQ framework with a novel Validate phase that introduces human oversight and interpretability into the ML decision-making loop. The modified framework has been implemented in a high-precision automotive component facility. The study relied on various ML algorithms, such as Decision Trees (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR), Support Vector Machine (SVM), and Artificial Neural Networks (ANN), to classify and predict defects in engine valves during manufacturing processes. The findings highlighted that GBM and RF provided the best performance, achieving an F1 score of 0.98 and an AUC of 0.99. Feature importance analyzes identified seat height and undercut diameter as key predictors, reinforcing the relevance of interpretable ML in industrial quality management. Beyond technical accuracy, this work demonstrates how structured human-machine collaboration can foster trust in AI-driven quality control, offering a scalable blueprint for Quality 4.0 adoption. The findings contribute to academic literature and industrial practice by bridging conceptual frameworks and real-world implementation strategies for AI-enhanced quality assurance.