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.

行业的最新发展涉及数字双胞胎的部署，以进行长期和短期决策，例如供应链管理，生产计划和控制。现代生产环境经常遭受破坏和随之而来的修改。结果，制造系统的数字双胞胎的开发不能仅仅依靠手动操作。最近的贡献提出了利用数据以自动生成模型的方法。但是，结果表示可能过于准确，并且可能还会描述对于估计系统性能而言并不重要的活动。生成具有适当详细程度的模型可以避免不必要的工作和较长的计算时间，同时还可以使理解和重用变得更容易。本文提出了一种自动发现制造系统并生成足够的数字孪生子的方法。生产系统的相关特征会自动从数据日志中检索。所提出的方法已应用于两个测试案例和一条实际生产线。实验结果证明了其在生成可以正确估计系统性能的数字模型方面的有效性。