Modeling and implementation of a digital twin of material flows based on physics simulation
| Autor: | Li Yi, Moritz Glatt, Bahram Ravani, Sean P. Donohoe, Jan C. Aurich, Chantal Sinnwell |
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| Rok vydání: | 2021 |
| Předmět: |
Flexibility (engineering)
0209 industrial biotechnology Decision support system business.product_category Computer science ComputerApplications_COMPUTERSINOTHERSYSTEMS Control engineering 02 engineering and technology Industrial and Manufacturing Engineering Domain (software engineering) Machine tool 020901 industrial engineering & automation Hardware and Architecture Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Dynamical simulation Physics engine business Throughput (business) Implementation Software |
| Zdroj: | Journal of Manufacturing Systems. 58:231-245 |
| ISSN: | 0278-6125 |
| Popis: | Cyber-physical production systems enable adaptivity and flexibility when manufacturing customized products in small batches. Due to varying routes and a high variance of workpieces, material flows in cyber-physical production systems can get highly complex, which can lead to physically induced disturbances that can result in accidents or decreased throughput and high costs. This issue can be addressed by applying a physics engine to simulate the physical interaction between workpieces and the material handling systems during the operation. Connecting such a digital model to a real material handling system in order to derive simulation-based decision support leads to the concept of digital twins. To date, few practical implementations of digital twins in manufacturing outside the machine tool domain were reported. Therefore, this paper describes the modeling and the subsequent implementation of an integrated system that consists of a real material handling system and its digital twin, based on physics simulation. A practical use case demonstrates the versatile advantages of the implemented solution for a manufacturing system with respect to the three digital twin functions prediction, monitoring and diagnosis. |
| Databáze: | OpenAIRE |
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