The Flexibility and Adaptability of the Digital Twin-Driven Manufacturing System in the Mass Customization Paradigm

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 1446

Special Issue Editors


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Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 100872, China
Interests: digital twin; manufacturing system; bionic manufacturing; intelligent machining

Special Issue Information

Dear Colleagues,

Digital twin (DT) has been a prevailing topic in both academics and industry, owing to the rapid development of artificial intelligence, cyber-physical systems, and digital technologies. The core idea of the DT is to develop a virtual replica corresponding to the physical entity. Through the real-time interaction between the virtual and real spaces, the system can display and analyse the state of the physical entity, and make further decisions on it. As an enabling technology of intelligent manufacturing, the DT has been used by many scholars to build the manufacturing system, that is, the DT-driven manufacturing system (DTMS). Previous research has demonstrated that DT has great potential in manufacturing.

Meanwhile, with the help of technological evolution and innovation, the rapid growth and globalization of Internet manufacturing services have created demanding customers who need unique, high-quality, low-cost products that meet their personal needs and preferences. Affected by the above, the mass customization (MC) paradigm has become a research hotspot in the manufacturing industry, and MC workshops are emerging in the manufacturing industry. MC combines the advantages of customized and mass production to meet customers' diversified and personalized needs with low cost and fast delivery. It aims to deliver products and services that meet individual manufacturing needs with near-mass production efficiency. Therefore, it is essential to provide personalized products or services for consumers through the flexibility and adaptability of manufacturing systems in the MC paradigm.

Therefore, due to the production needs of multiple varieties and small batches in the MC paradigm, the DTMS faces challenges on flexibility and adaptability. This special session aims to bring together specialists in the flexibility and adaptability of DTMS to address the large-scale customization paradigm.

This special issue aims to present the state-of-the-art, theories, informatics-based approaches, tools, and cases to focus on the flexibility and adaptability of digital twin-driven manufacturing system. To contribute to those areas, this special issue includes the following topics, but are not limited to:

  • Digital twin-driven decision-making in the manufacturing process
  • Digital twin-driven self-organization between multiple manufacturing systems
  • Digital twin-driven self-adaption for dynamically changing requirements
  • Digital twin-driven quality inspection in the manufacturing process
  • Digital twin-driven collaboration among multiple manufacturing systems
  • Digital twin-driven autonomous decision-making based on multimodal information analysis

Dr. Shimin Liu
Prof. Dr. Jinsong Bao
Guest Editors

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Keywords

  • digital-twin-driven manufacturing system
  • mass customization paradigm
  • flexibility
  • adaptability

Published Papers (1 paper)

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Research

16 pages, 3576 KiB  
Article
Digital-Twin-Based Monitoring System for Slab Production Process
by Tianjie Fu, Peiyu Li, Chenke Shi and Youzhu Liu
Future Internet 2024, 16(2), 59; https://doi.org/10.3390/fi16020059 - 13 Feb 2024
Viewed by 1149
Abstract
The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking [...] Read more.
The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking industry’s processes is relatively insufficient. Consequently, slab ingot quality inspection is characterized by high-temperature risks and imprecision. The positional accuracy of quality detection is inadequate, and the precise quantification of slab ingot production and quality remains challenging. This paper proposes a digital twin (DT)-based monitoring system for the slab ingot production process that integrates DT technology with slab ingot process detection. A neural network is introduced for defect identification to ensure precise defect localization and efficient recognition. Concurrently, environmental production factors are considered, leading to the introduction of a defect prediction module. The effectiveness of this system is validated through experimental verification. Full article
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