Security in modern manufacturing systems: integrating blockchain in artificial intelligence-assisted manufacturing

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.

GRAPHICAL ABSTRACT

Keywords:

• Blockchain
• industry 4.0
• machine learning
• manufacturing
• security

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement (DAS)

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Notes

1 https://www.adidas.com/us/creatorsclub

2 https://www.nike.com/nike-by-you

3 https://cloudnc.com/

4 https://www.xometry.com/

5 https://www.skuchain.com/

6 https://www.adidas.com/us/creatorsclub

7 https://www.nike.com/nike-by-you

Additional information

Notes on contributors

Dhruv Patel

Dhruv Patel received his Master of Science degree in Mechanical Engineering from State University of New York at Buffalo, USA, in 2021. His thesis research focused on ‘Blockchain Based Secure Machine Learning Model Integration For Collaborative Manufacturing’.He is currently working as Data Process Engineer at Radiometer America in California, USA. In this role, he analyses complex production datasets to improve processes and product reliability working along with the R&D team. He specialises in the analysis and interpretation of intricate data sets derived from diverse production processes and equipment. His expertise lies in harnessing the power of manufacturing insights and data analytics to propose enhancements in processes, modifications in measurement techniques, and design refinements, all aimed at elevating product manufacturability and reliability. His work involves close collaboration with cross-functional teams, including Quality, Research and Development, and Production, to identify and implement valuable opportunities for enhancement.

Chandan Kumar Sahu

Chandan Kumar Sahu is currently pursuing his Ph.D. degree in automotive engineering at Clemson University. He received his bachelors from National Institute of Technology Rourkela, India, in 2014, and his masters from the State University of New York at Buffalo, USA, in 2020, both in mechanical engineering. He led the homologation of the development of new models at Honda Motorcycle and Scooter India from 2014 to 2017.He is interested in machine learning in general, with a focus on its geometric attributes and mechanical applications. His research investigated additive manufacturing, augmented reality, cyber-physical systems, and engineering design applications using computational tools like machine learning, graph theory, and natural language processing. His current research aspires to develop mathematical models of requirements to capture their interrelationships to investigate the role of requirements during the evolution of a system.?

Rahul Rai

Rahul Rai joined the Department of Automotive Engineering in 2020 as Dean's Distinguished Professor in the Clemson University International Center for Automotive Research (CU-ICAR). He also holds appointments in the Computer Science and Mechanical Engineering departments at Clemson University. He is Associate Director of Artificial Intelligence Research Institute in Science and Engineering (AIRISE) at Clemson University. He directs the Geometric Reasoning and Artificial Intelligence Lab (GRAIL, which is located at both CU-ICAR and Center for Manufacturing Innovation (CMI). Previously, he served on the Mechanical and Aerospace Engineering faculty at the University at Buffalo-SUNY (2012–2020). Dr. Rai also has industrial research centre experiences at United Technology Research Center (UTRC) and Palo Alto Research Center (PARC). Dr. Rai received his B.Tech. degree in 2000 and M.S. degree in 2002 in Manufacturing Engineering from the National Institute of Foundry and Forge Technology (NIFFT), Ranchi, India, and Missouri University of Science and Technology (Missouri S&T) USA, respectively. He earned his doctoral degree in Mechanical Engineering from The University of Texas at Austin USA in 2006.Dr. Rai's research is focused on developing computational tools for Manufacturing, Cyber-Physical System (CPS) Design, Autonomy, Collaborative Human-Technology Systems, Diagnostics and Prognostics, and Extended Reality (XR) domains. By combining engineering innovations with methods from machine learning, AI, statistics and optimisation, and geometric reasoning, his research strives to solve important problems in the above-mentioned domains. His research has been supported by NSF, DARPA, ONR, ARL, NSWC, DMDII, CESMII, HP, NYSERDA, and NYSPII (funding totalling more than $20M as PI/Co-PI). He has authored over 100 papers to date in peer-reviewed conferences and journals covering a wide array of problems. Dr. Rai is the recipient of numerous awards, including the 2009 HP Design Innovation, 2017 ASME IDETC/CIE Young Engineer Award, and 2019 PHM society conference best paper award. Additionally, Dr. Rai is Associate Editor of the International Journal of Production Research and ASME Journal of Computing and Information Science in Engineering (JCISE) journals and has taken significant leadership roles within the ASME Computers and Information in Engineering professional society.