A blockchain-enabled deep residual architecture for accountable, in-situ quality control in industry 4.0 with minimal latency

Real-time and vision-based quality control for industrial processes has drawn great interest from both scientists and practitioners, particularly following the transition to Zero Defect Manufacturing (ZDM) and Industry 4.0. Despite considerable progress, most ZDM approaches focus on the accuracy of...

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Bibliographic Details
Published in:	Computers in industry Vol. 149; p. 103919
Main Authors:	Leontaris, Lampros, Mitsiaki, Andreana, Charalampous, Paschalis, Dimitriou, Nikolaos, Leivaditou, Eleni, Karamanidis, Aristoklis, Margetis, George, Apostolakis, Konstantinos C., Pantoja, Sebastian, Stephanidis, Constantine, Tzovaras, Dimitrios, Papageorgiou, Elpiniki
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-08-2023
Subjects:	Blockchain IoT Deep residual networks Industry 4.0 Quality control Zero-defect manufacturing Industry 4.0 Zero-defect manufacturing Blockchain IoT Quality control Deep residual networks
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Summary:	Real-time and vision-based quality control for industrial processes has drawn great interest from both scientists and practitioners, particularly following the transition to Zero Defect Manufacturing (ZDM) and Industry 4.0. Despite considerable progress, most ZDM approaches focus on the accuracy of the inspection process, often neglecting critical factors for application in the shop floor. On one hand, near real-time methods are needed for early defect detection and containment. On the other hand, data scarcity is an issue causing AI methods to overfit. Another concern is the accountability of AI results, since even if an AI pipeline is successfully deployed, its predictions are not verifiable in the long term. In this work, we explore a real-time solution based on lightweight Deep Residual Networks and Blockchain technology to address these issues. Concretely, we propose a two-phase training strategy to boost the performance of baseline classifiers while maintaining low inference times. The performance of the proposed methodology is presented in two different industrial use cases with strict timing requirements, one concerning battery assembly line and the other antenna manufacturing. We validate the proposed method for defect detection and compare the results with common training strategies demonstrating an improvement of 3% and 10% in F1-score and accuracy on the two cases respectively, while lowering inference time by 2.2× compared to existing light architectures. Contributing to the accountability of AI results, we present an IoT framework using Blockchain deployed in Private Ethereum. •A lightweight defect detection method with minimal inference latency.•A two-phase feature learning strategy is proposed based on Deep Residual Networks.•Feature extraction capabilities are instilled from larger networks to more lightweight architectures.•An integration scheme to record and retrieve AI results using Blockchain in private Ethereum.
ISSN:	0166-3615 1872-6194
DOI:	10.1016/j.compind.2023.103919