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Shaw C. Feng, Albert T. Jones, Guodong Shao


The number and types of sensors used to monitor additive manufacturing (AM) processes and parts in real time are growing. The emerging digital twins (DTs) associated with the data collected by those sensors and the functions that use that data as inputs are becoming increasingly important research topics. There are fast growing demands across several industry sectors to develop software tools that can create, fuse, and measure both AM DTs and digital threads across the entire AM part lifecycle. While some tools are available, they are not well correlated to part-qualification functions in that lifecycle. The accuracy of the DT tools, the fidelity of their inputs, and the ability to create digital threads are still open research questions. The goal of this paper is to identify the data requirements and technical barriers that are limiting the ability to qualify real AM parts using DTs and digital threads. The data requirements can be used as a guide for AM users to create both. This paper uses laser-based powder bed fusion AM for metals as a case study to help identify those requirements and create those digital twins and threads.
Conference Dates
June 12-16, 2023
Conference Location
New Brunswick, NJ, US
Conference Title
ASME Manufacturing Science and Eningeering Conference 2023


additive manufacturing, data requirements, digital twin, part qualification


Feng, S. , Jones, A. and Shao, G. (2023), DATA REQUIREMENTS FOR DIGITAL TWINS IN ADDITIVE MANUFACTURING, ASME Manufacturing Science and Eningeering Conference 2023, New Brunswick, NJ, US, [online], (Accessed April 14, 2024)
Created June 15, 2023, Updated July 9, 2023