Design and Development of Automobile Assembly Model Using Federated Artificial Intelligence with Smart Contract

A. Manimuthu, V.G. Venkatesh, Y. Shi, V.R. Sreedharan and S.C.L. Koh
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V.G. Venkatesh: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

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Abstract: With smart sensors and embedded drivers, today's automotive industry has taken a giant leap in emerging technologies like Machine learning, Artificial intelligence, and the Internet of things and started to build data-driven decision-making strategies to compete in global smart manufacturing. This paper proposes a novel design framework that uses Federated learning-Artificial intelligence (FAI) for decision-making and Smart Contract (SC) policies for process execution and control in a completely automated smart automobile manufacturing industry. The proposed design introduces a novel element called Trust Threshold Limit (TTL) that helps moderate the excess usage of embedded equipment, tools, energy, and cost functions, limiting wastages in the manufacturing processes. This research highlights the use cases of AI in decentralised Blockchain with smart contracts, the company's trading policies, and its advantages for effectively handling market risk assessments during socio-economic crisis. The developed model supported by real-time cases incorporated cost functions, delivery time and energy evaluations. Results spotlight the use of FAI in decision accuracy for the developed smart contract-based Automobile Assembly Model (AAM), thereby qualitatively limiting the threshold level of cost, energy and other control functions in procurement assembly and manufacturing. Customisation and graphical user interface with cloud integration are some challenges of this model. \textcopyright 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Keywords: Artificial intelligence; blockchain; federated machine learning; original equipment manufacturer; smart contract (search for similar items in EconPapers)
Date: 2022
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Published in International Journal of Production Research, 2022, 60 (1), pp.111-135. ⟨10.1080/00207543.2021.1988750⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04435625

DOI: 10.1080/00207543.2021.1988750

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