Automated manufacturing system discovery and digital twin generation

• Published in: Journal of manufacturing systems Vol. 59; pp. 51 - 66
• Main Authors: Lugaresi, Giovanni, Matta, Andrea
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2021; Elsevier
• Subjects: Computational Engineering, Finance, and Science; Computer Aided Engineering; Computer Science; Digital twins; Industry 4.0; Manufacturing systems; Process mining; Simulation; Process mining; Industry 4.0; Digital twins; Simulation; Manufacturing systems; Digital Twins Industry 4.0 Simulation Process Mining; Digital Twins; Process Mining
• ISSN: 0278-6125; 1878-6642
• DOI: 10.1016/j.jmsy.2021.01.005

•Method to automatically discover manufacturing systems structure from data.•Automatic generation of digital twins with an appropriate level of detail.•Characteristics of production systems automatically retrieved from data.•Model tuning method that generates models able to estimate system performances.•1-min model development allows for online application. The latest developments in industry involved the deployment of digital twins for both long and short term decision making, such as supply chain management, production planning and control. Modern production environments are frequently subject to disruptions and consequent modifications. As a result, the development of digital twins of manufacturing systems cannot rely solely on manual operations. Recent contributions proposed approaches to exploit data for the automated generation of the models. However, the resulting representations can be excessively accurate and may also describe activities that are not significant for estimating the system performance. Generating models with an appropriate level of detail can avoid useless efforts and long computation times, while allowing for easier understanding and re-usability. This paper proposes a method to automatically discover manufacturing systems and generate adequate digital twins. The relevant characteristics of a production system are automatically retrieved from data logs. The proposed method has been applied on two test cases and a real manufacturing line. The experimental results prove its effectiveness in generating digital models that can correctly estimate the system performance.