AI Algorithms for Early Detection of Defective ECUs in Automotive Production Lines

Abstract

The reliability of automotive Electronic Control Units (ECUs) is critical for ensuring vehicle safety, performance, and manufacturing efficiency. Conventional end-of-line (EOL) functional testing detects defects only after full production, resulting in costly rework, production delays, and increased warranty exposure. This paper presents a machine learning–based framework for the early detection of defective ECUs during automotive manufacturing. The proposed system integrates multi-source production data including in-line sensor measurements, environmental parameters, assembly process logs, and electrical test signals to predict ECU defects prior to EOL verification. Multiple supervised learning models were developed and evaluated, with ensemble methods such as Random Forest and Gradient Boosting demonstrating over 96% defect detection accuracy, achieving high recall for failure classes while maintaining low false-positive rates. A pilot deployment on an active production line showed a 22% reduction in rework costs and a 14% improvement in throughput, attributed to earlier intervention and reduced workflow disruptions. The results demonstrate that AI-driven defect prediction can shift quality assurance toward a proactive, zero-defect paradigm, aligning with smart manufacturing objectives and supporting automotive industry demands for higher reliability and operational efficiency. The proposed framework provides a scalable and explainable solution for real-time quality prediction, offering significant economic and operational benefits for modern ECU production environments