Manufacturing resilience and agility through processes digital twin: design and testing applied in the LPBF case
Alexios Papacharalampopoulos, Christos K. Michail, Panagiotis Stavropoulos
Abstract
Digital twins can be very promising towards offering agility and resilience in manufacturing. Especially at process level, the adaptability and the real-time optimization they can bring along is highly desired. However, digital twins, being the outcome of severe systems integration are difficult to be designed and be implemented integrating all the desired functions, such as process control and quality assessment. This work investigates the opportunity to integrate different models under the concept of a digital twin of a manufacturing process (namely LPBF) and be able to meet diverse requirements, such as adaptivity, real-time optimization and uncertainty management. The suggested framework takes into account all the phases of the digital twin, such as sensorization, modelling, diagnostic and prognostic functions and puts together an architecture including all available models and thus paving the way towards achieving to meet all the requirements. A case study is also presented showing the capabilities of the partial models utilized as well as the performance of the digital twin in closed-loop control. The digital twin is proved to be highly feasible and appears to have good performance indicators.