期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 60, 期 1, 页码 111-135出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2021.1988750
关键词
Artificial intelligence; blockchain; federated machine learning; original equipment manufacturer; smart contract
资金
- Academy of the Social Sciences in Australia
The automotive industry has made significant advances in emerging technologies and is using FAI and SC policies for decision-making and process control to limit wastages. Using decentralized Blockchain and smart contracts, the study highlights the benefits of AI in handling market risk assessments during economic crises.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据