Affiliation:
1. Artificial Intelligence for Engineering Science Team, Moulay Ismail University, Meknes 50050, Morocco
2. Mathematics, Computer Science and Engineering Department, University of Quebec at Rimouski, Rimouski, QC G5L 3A1, Canada
Abstract
In the context of Industry 4.0 and smart manufacturing, production factories are increasingly focusing on process optimization, high product customization, quality improvement, cost reduction, and energy saving by implementing a new type of digital solutions that are mainly driven by Internet of Things (IoT), artificial intelligence, big data, and cloud computing. By the adoption of the cyber–physical systems (CPSs) concept, today’s factories are gaining in synergy between the physical and the cyber worlds. As a fast-spreading concept, a digital twin is considered today as a robust solution for decision-making support and optimization. Alongside these benefits, sectors are still working to adopt this technology because of the complexity of modeling manufacturing operations as digital twins. In addition, attempting to use a digital twin for fully automatic decision-making adds yet another layer of complexity. This paper presents our framework for the implementation of a full-duplex (data and decisions) specific-purpose digital twin system for autonomous process control, with plastic injection molding as a practical use-case. Our approach is based on a combination of supervised learning and deep reinforcement learning models that allows for an automated updating of the virtual representation of the system, in addition to an intelligent decision-making process for operational metrics optimization. The suggested method allows for improvements in the product quality while lowering costs. The outcomes demonstrate how the suggested structure can produce high-quality output with the least amount of human involvement. This study shows how the digital twin technology can improve the productivity and effectiveness of production processes and advances the use of the technology in the industrial sector.
Subject
Information Systems and Management,Computer Networks and Communications,Modeling and Simulation,Control and Systems Engineering,Software
Reference54 articles.
1. Cheng, K. (2022). Digital-Twins-Driven Semi-Physical Simulation for Testing and Evaluation of Industrial Software in a Smart Manufacturing System. Machines, 10.
2. Digital twins-based smart manufacturing system design in Industry 4.0: A review;Leng;J. Manuf. Syst.,2021
3. Virtual twins as integrative components of smart products;Abramovici;Product Lifecycle Management for Digital Transformation of Industries: 13th IFIP WG 5.1 International Conference, PLM 2016, Columbia, SC, USA, 11–13 July 2016, Revised Selected Papers,2016
4. About the importance of autonomy and digital twins for the future of manufacturing;Rosen;IFAC-Paper,2015
5. Zhang, Q., Liu, Z., Duan, J., and Qin, J. (2023). A Novel Method of Digital Twin-Based Manufacturing Process State Modeling and Incremental Anomaly Detection. Machines, 11.