Agentic AI for Self-Healing Production Lines: Autonomous Root Cause Analysis & Correction

Abstract

Modern automotive manufacturing demands minimal downtime and near-zero defects. This paper proposes an agentic AI framework for self-healing production lines, enabling autonomous fault diagnosis and correction in real time. We integrate agent-based models, causal inference, and self-adaptive control to monitor processes, identify root causes of faults, and adjust controls without human intervention. The architecture is demonstrated in scenarios like body-in-white welding and final assembly inspection, where AI agents at robotic stations collaborate with a supervisor agent to detect quality issues (e.g., bad welds, misalignments), infer underlying causes, and enact corrective actions (like recalibration or parameter tuning). A novel case study is presented in which an autonomous welding cell agent detects a weld defect, determines tip wear as root cause, and triggers an on-the-fly tool change and re-weld—preventing downtime. We report substantial improvements: fault response times drop from tens of minutes to seconds, process recovery becomes nearly instantaneous, and overall equipment effectiveness (OEE) rises with reduced scrap and downtime. Five high-quality images, three charts, and three diagrams illustrate the agentic system architecture, decision loops, fault response performance, and comparative benchmarks. The proposed framework—unlike any published to date—demonstrates a unique, self-healing manufacturing AI that achieves resilient, “right-first-time” production in automotive assembly.