Digital Twin Modelling for Optimizing the Material Consumption: A Case Study on Sustainability Improvement of Thermoforming Process

Author(s):  
Erhan Turan ◽  
Yiğit Konuşkan ◽  
Nihan Yıldırım ◽  
Deniz Tunçalp ◽  
Mehmet İnan ◽  
...  
2021 ◽  
Vol 11 (10) ◽  
pp. 4620
Author(s):  
Niki Kousi ◽  
Christos Gkournelos ◽  
Sotiris Aivaliotis ◽  
Konstantinos Lotsaris ◽  
Angelos Christos Bavelos ◽  
...  

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.


2021 ◽  
Vol 223 ◽  
pp. 108629
Author(s):  
Demetrious T. Kutzke ◽  
James B. Carter ◽  
Benjamin T. Hartman

2021 ◽  
Vol 51 (1) ◽  
pp. 20210043
Author(s):  
Wynand JvdM Steyn ◽  
André Broekman
Keyword(s):  

2021 ◽  
Vol 9 (1) ◽  
pp. 140-156
Author(s):  
Santiago Martinez ◽  
Alexis Mariño ◽  
Sofia Sanchez ◽  
Ana María Montes ◽  
Juan Manuel Triana ◽  
...  

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 9
Author(s):  
Yuchen Wang ◽  
Xingzhi Wang ◽  
Fei Tao ◽  
Ang Liu

Complexity management is one of the most crucial and challenging issues in manufacturing. As an emerging technology, digital twin provides an innovative approach to manage complexity in a more autonomous, analytical and comprehensive manner. This paper proposes an innovative framework of digital twin-driven complexity management in intelligent manufacturing. The framework will cover three sources of manufacturing complexity, including product design, production lines and supply chains. Digital twin provides three services to manage complexity: (1) real-time monitors and data collections; (2) identifications, diagnoses and predictions of manufacturing complexity; (3) fortification of human-machine interaction. A case study of airplane manufacturing is presented to illustrate the proposed framework.


Author(s):  
Siavash H. Khajavi ◽  
Alireza Jaribion ◽  
Adriaan Knapen ◽  
Leila Abiedat
Keyword(s):  

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