Security in modern manufacturing systems: integrating blockchain in artificial intelligence-assisted manufacturing.
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| Title: | Security in modern manufacturing systems: integrating blockchain in artificial intelligence-assisted manufacturing. |
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| Authors: | Patel, Dhruv1 (AUTHOR), Sahu, Chandan Kumar2 (AUTHOR), Rai, Rahul2,3 (AUTHOR) rrai@clemson.edu |
| Source: | International Journal of Production Research. Feb2024, Vol. 62 Issue 3, p1041-1071. 31p. |
| Subjects: | Manufacturing processes, Blockchains, Artificial intelligence, Product counterfeiting, Consumer ethics |
| Abstract: | Process automation and mass customisation requirements of modern manufacturing systems are driven by artificial intelligence (AI). As AI derives decisions from data, securing the data against tampering is crucial to prevent ensuing operational risks. Additionally, manufacturing systems necessitate collaboration, transparency, and trust among participants while preserving a competitive advantage. Thus, we position blockchain, an enabler of transparent and secure operations, as a security solution for AI-assisted manufacturing systems. In this conceptual viewpoint paper, we present a framework to integrate blockchain in AI-assisted manufacturing systems. We highlight the special needs of manufacturing BCs over generic BCs. We delineate the ways in which manufacturing can be a beneficiary of the synergy between AI and BC. We discuss how BC and AI can accelerate early-phase product design, collaboration, and manufacturing processes and secure supply chains against counterfeit products and for ethical consumerism. Lastly, we identify the needs of modern manufacturing systems and cite a few examples of organisational failures to underscore the importance of security while delineating the significant challenges in adopting blockchain-based solutions in the manufacturing industry. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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