This study evaluates South Korea’s global competitiveness in machine learning applications for manufacturing quality management in the context of the transition to ‘Quality 4.0’ using a bibliometric analysis. A total of 1,624 articles, including 99 Korean studies, published between 2010 to 2025 were analyzed using Scopus data and a two-stage AI-assisted screening process. This study introduces a novel three-stage Machine Learning Maturity Framework (Level 1~3) and an time constraints analysis to enable an in-depth assessment of qualitative maturity levels. The results show that Korea ranks fifth globally in research volume and demonstrates world-class competitiveness in Level 1 (Defect Detection) and Level 2 (Virtual Metrology), particularly in the semiconductor and electronics industries, which primarily drive this competitiveness. However, whereas global research is increasingly shifting toward process optimization across diverse domains, Korea exhibits a relatively low proportion of Level 3 (Control) studies. Furthermore, although online monitoring of field data is prevalent (45.5%), real-time closed-loop control remains limited (20.2%), indicating a technological gap between sensing and control. Consequently, this study leverages Korea's strengths in data infrastructure and inspection technologies to propose an industry-specific roadmap for transitioning from monitoring-centric toward explainable autonomous control and PMQ (Production-Maintenance-Quality) integrated systems.
Sung-Hwan Jung (Fri,) studied this question.