Applying Industrial AI for Proactive Quality Control of ECUs in Automotive Production

Veera Venkata Krishnarjun Rao Adabala

doi:10.71097/IJSAT.v12.i3.6986

Applying Industrial AI for Proactive Quality Control of ECUs in Automotive Production

Author(s)	Veera Venkata Krishnarjun Rao Adabala
Country	United States
Abstract	Electronic Control Units (ECUs) are the backbone of critical automotive subsystems, including engine management, braking, safety, infotainment, and advanced driver assistance systems (ADAS). As vehicles become increasingly software-defined and electronically controlled, the reliability of ECUs during manufacturing becomes paramount. Traditional quality control methods, while effective to an extent, often rely on rule-based testing and post-failure analysis, which can miss subtle defect patterns and lead to costly field failures, warranty claims, or recalls.This paper proposes a proactive quality assurance framework driven by Industrial Artificial Intelligence (AI), designed to enhance defect detection accuracy during ECU manufacturing. The approach integrates supervised machine learning models and anomaly detection algorithms trained on high-dimensional datasets collected throughout the production line and End-of-Line (EOL) functional testing. These models learn to identify complex, nonlinear relationships within test parameters that are indicative of potential ECU defects—often before the failure manifests during traditional testing procedures. A case study conducted in an operational automotive production environment demonstrates the efficacy of the system. The AI-based method achieved an increase in early defect detection rates by over 30%, while significantly reducing false positives, leading to improved operational efficiency and reduced manual rework. By enabling real-time, data-driven decisions, this methodology aligns with Industry 4.0 objectives and offers a scalable solution for predictive quality control in high-volume automotive manufacturing.
Keywords	Electronic Control Units (ECUs), Defect Detection, End-of-Line (EOL) Testing, Machine Learning, Supervised Learning, Unsupervised Learning, Random Forest, Support Vector Machines (SVMs), Autoencoders, Anomaly Detection, Quality Assurance, ASPICE Compliance, ISO 26262, Explainable AI (XAI), Edge Computing.
Field	Engineering
Published In	Volume 12, Issue 3, July-September 2021
Published On	2021-08-06
DOI	https://doi.org/10.71097/IJSAT.v12.i3.6986
Short DOI	https://doi.org/g9vddk

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Applying Industrial AI for Proactive Quality Control of ECUs in Automotive Production

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