The Impact of Artificial Intelligence-Based Quality Control Systems on Production Efficiency in the Context of Industry 4.0: An Empirical Study

This study presents an integrated quality control architecture designed in line with Industry 4.0 principles for an automotive-focused production line. The architecture unifies micron-level dimensional inspection via air gauges, assembly verification through CNN-based computer vision, and full traceability based on DMC/QR + OCR within a single decision layer. A three-tier software stack (data acquisition, processing/analysis, decision/feedback) operates in real time through a microservices architecture with MES/PLC integration. Decision fusion triggers the right intervention without stopping the line through an “accept–gray zone–segregate” policy; SPC-based online monitoring makes minor drifts visible at an early stage. The implementation increased assembly accuracy and measurement reliability, strengthened traceability by catching duplicate identity assignments in process, reduced the risk of shipping defects, and lowered manual inspection burden. By integrating measurement, visual, and identity data into a single traceability chain, the study proposes a practical and scalable model for transitioning from reactive inspection to proactive/preventive quality assurance.