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. 2025 Jul 9;15(1):24742.
doi: 10.1038/s41598-025-10226-4.

Implementing and evaluating the quality 4.0 PMQ framework for process monitoring in automotive manufacturing

Fathy Alkhatib  1 , Mohamed Allam  1 , Vikas Swarnakar  2 , Juman Alsadi  1 , Maher Maalouf  1
Affiliations

Implementing and evaluating the quality 4.0 PMQ framework for process monitoring in automotive manufacturing

Fathy Alkhatib et al. Sci Rep. .

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.

Keywords: Industry 4.0; Machine learning; Manufacturing process optimization; Predictive maintenance; Process monitoring for quality framework; Quality 4.0.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Technical illustration of an engine valve.
Fig. 2
Fig. 2
PMQ cycle.
Fig. 3
Fig. 3
Histogram plot for each key feature.
Fig. 4
Fig. 4
Process capability for each key process.
Fig. 5
Fig. 5
X-bar/S control chart for each key process.
Fig. 6
Fig. 6
Hotelling T² control chart.
Fig. 7
Fig. 7
ROC curve.
Fig. 8
Fig. 8
Performance comparison of employed models using F1 score and AUC.
Fig. 9
Fig. 9
Feature importance bar chart for RF model.
Fig. 10
Fig. 10
Feature importance Bar chart for GBM model.
See this image and copyright information in PMC

References

    1. Alkhatib, F. Y., Alsadi, J. K., Ramadan, M. A., Antony, J. & Swarnakar, V. Industry 4.0 applications in the healthcare sector: the dawn of healthcare 4.0. In Green Manufacturing for Industry 4.0 (eds Parihar, R. S. & Jain, N.) 51–60 (Productivity, 2024).
    1. Alsadi, J. et al. A systematic literature review with bibliometric analysis of Quality 4.0. TQM J.37(5), 1446–1470. 10.1108/TQM-02-2024-0050 (2025). - DOI
    1. Antony, J. et al. Sustainable development through quality management: a multiple-case study analysis of triumphs, trials and tribulations. TQM J.37 (4), 905–925 (2025).
    1. Antony, J. et al. Benefits, challenges, critical success factors and motivations of quality 4.0–A qualitative global study. Total Qual. Manage. Bus. Excellence. 34 (7–8), 827–846 (2023a).
    1. Antony, J., Sony, M., McDermott, O., Jayaraman, R. & Flynn, D. An exploration of organizational readiness factors for quality 4.0: an intercontinental study and future research directions. Int. J. Qual. Reliab. Manage.40 (2), 582–606 (2023b).

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