AI+IoT+Blockchain Triad for Smart Traceability in the Automotive Industry

Kevin Patel
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Kevin Patel: Mechanical Engineering, USA

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 7, 221-238

Abstract: The convergence of Artificial Intelligence (AI), Internet of Things (IoT), and blockchain is driving a new paradigm for traceability in automotive manufacturing. This paper presents a tri-layer integrated system employing IoT sensors for real-time data capture on a cowl stamping line, AI models for defect detection and process anomaly diagnosis, and blockchain for secure, tamper-proof traceability of part quality records. The proposed framework leverages IoT-enabled digital twins and AI-driven analytics to monitor stamping conditions and detect defects, while blockchain smart contracts ensure immutable documentation of each part’s production data and any quality alerts. We detail the system architecture and data flow, the AI model training and deployment, and the blockchain network implementation for the stamping supply chain. A case study on an automotive cowl stamping process demonstrates the triad’s effectiveness: IoT sensors continuously feed process parameters to AI algorithms that identify anomalies (e.g., force spikes, temperature deviations) and trigger blockchain transactions logging these events. Results show improved defect detection accuracy (over 90%) and end-to-end traceability that can mitigate counterfeit parts and quality disputes. The integration of AI+IoT+Blockchain thus enhances visibility and trust in manufacturing processes, paving the way for smarter, more transparent automotive production networks.

Date: 2025
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