Special Section on Probabilistic Digital Twins in Additive Manufacturing

Abstract

Additive manufacturing (AM) has made enormous progress over the past decade, as it is capable of producing complex parts with significantly fewer fabrication constraints compared with existing manufacturing technologies over a broad dimensional scale. AM's innate manufacturing flexibility has a significant impact on time and cost savings, as well as inventory, supply chain management, assembly, and maintenance for demanding engineering applications. Complicated AM process variability is one of the greatest obstacles in performance evaluation, quality control, and certification of additively manufactured materials and products, and thus hinders the widespread implementation of AM techniques. Digital twin, as a digital replica of a production system or an active unique product characterized by certain properties or conditions, has great potentials in overcoming the quality variability and reliability issues in AM processes. With the development of probabilistic digital twins in AM and uncertainty management techniques, it becomes possible to realize robust and reliable AM process by optimizing process parameters, detecting, and monitoring process faults, reducing the computational burden for multiscale modeling, and dealing with the large volume of in situ sensor data. This special issue is dedicated to recent advances in the field of digital twins and uncertainty management with application in additive manufacturing.