Blockchain-enabled digital twin collaboration platform for heterogeneous socialized manufacturing resource management

Abstract

Social manufacturing is conceptualised as a new type of networked manufacturing paradigm that supports the organisation of socialised manufacturing resources (SMRs) to satisfy the growth of personalised demands on crowd intelligence in a timely manner when co-creating open architecture products. The high participation of each stakeholder in this type of product production places a higher requirement on sufficient collaboration among social, cyber and physical spaces. However, under a decentralised social manufacturing network, the management of SMRs has encountered some real-life challenges in terms of their distributed and heterogeneous features. Hence, this paper proposes a blockchain-enabled digital twin collaboration platform (BcDTCP) as an integrated solution to address these challenges. A hybrid domain-driven design method is presented to design and implement business-knowledge driven systems. To address the heterogeneity of SMRs, a ubiquitous object structure is designed to flexibly adjust the functionality of the digital twin. Blockchain is introduced to construct a peer-to-peer network to organise the SMRs in a decentralised manner. Additionally, a timed coloured Petri net-based workflow is adopted to formulise the collaboration logic into a smart contract executed on the blockchain. Finally, a demonstrative case study is conducted to verify and evaluate the proposed BcDTCP under a 3D printing scenario.

KEYWORDS:

• Blockchain
• digital twin
• social manufacturing
• domain driven design
• resource collaboration

Acknowledgments

This research is supported by the National Natural Science Foundation of China (No. 52005218), the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515110296), Fundamental Research Funds for the Central Universities of China (No. 21620359) and the National Key R&D Program of China (No. 2019YFB1705401).

Disclosure statement

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

Additional information

Funding

This work was supported by Natural Science Foundation of China: [Grant Number 52005218]; Natural Science Foundation of Guangdong Province, China: [Grant Number 2019A1515110296]; Fundamental Research Funds for the Central Universities of China: [Grant Number 21620359]; National Key R&D Program of China: [Grant Number 2019YFB1705401].

Notes on contributors

Ming Li

Dr. Ming Li is a lecturer at School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus). He received his PhD degree and master degree from Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong in 2018 and 2013 respectively. He also obtained two Bachelor degrees in computer science and finance respectively at South China University of Technology. Prior to joining JNU at 2019, he was a post-doctoral fellow at Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong. His research interests include multi-agent system, digital twin and blockchain technology.

Yelin Fu

Dr. Yelin Fu is an Associate Professor at School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus). Dr. Fu obtained two Ph.D. degrees in Management Science from City University of Hong Kong, and Management Science and Engineering from University of Science and Technology of China in 2017. His current research interests are Decision Support System, Blockchain Technology, Operations Management.

Qiqi Chen

Ms. Qiqi Chen is a PhD candidate at Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong. She received his master and bachelor degree from School of Computer Science and Engineering, South China University of Technology in 2018 and 2013 respectively. Her current research interests include Cyber-Physical Systems and spare parts logistics.

Ting Qu

Prof. Ting Qu is a full professor at School of Intelligent Systems Science and Engineering, Jinan University. He received his BEng and MPhil degrees from School of Mechanical Engineering of Xi'an Jiaotong University, and obtained PhD degree from the department of Industrial and Manufacturing Systems Engineering of The University of Hong Kong. After taking the positions of postdoctoral research fellow and research assistant professor at HKU, he was appointed as a full professor in 2010 and the department head of Industrial Engineering in 2014 at Guangdong University of Technology. In 2016, he moved to Jinan University. Prof. Qu's research interests include IoT-based smart manufacturing systems, logistics and supply chain management, and industrial product/production service systems.