Design and development of automobile assembly model using federated artificial intelligence with smart contract


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.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences
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Additional Information: © 2021 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. Funding Information: Yangyan Shi acknowledged that the grant for ‘research on the supply of market-oriented elderly care services with involvements of financial institutions’ was supported by funding from the Academy of the Social Sciences in Australia.
Uncontrolled Keywords: Artificial intelligence,blockchain,federated machine learning,original equipment manufacturer,smart contract,Strategy and Management,Management Science and Operations Research,Industrial and Manufacturing Engineering
Publication ISSN: 1366-588X
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Related URLs: https://www.tan ... 43.2021.1988750 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-01
Published Online Date: 2021-10-26
Accepted Date: 2021-09-24
Authors: Manimuthu, Arunmozhi (ORCID Profile 0000-0003-4909-4880)
G Venkatesh, V
Shi, Yangyan
Sreedharan, V Raja
Koh, S. C.Lenny

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