Measurement Science Needs for Real-time Control of Additive Manufacturing Powder Bed Fusion Processes
Mahesh Mani, Brandon M. Lane, M A. Donmez, Shaw C. Feng, Shawn P. Moylan, Ronnie R. Fesperman Jr.
Additive Manufacturing is increasingly used in the development of new products: from conceptual design to functional parts and tooling. However, today, variability in part quality due to inadequate dimensional tolerances, surface roughness, and defects, limits its broader acceptance for high-value or mission-critical applications. While process control in general can limit this variability, it is impeded by a lack of adequate process measurement methods. Process control today is based on heuristics and experimental data, yielding limited improvement in part quality. The overall goal is to develop the measurement science necessary to make in-process measurement and real-time control possible in additive manufacturing. Traceable dimensional and thermal metrology methods must be developed for real-time closed-loop control of additive manufacturing processes. As a precursor, this report presents a review on the additive manufacturing control schemes, process measurements, and modeling and simulation methods as it applies to the powder bed fusion process, though results from other processes are reviewed where applicable. The aim of the review is to identify and summarize the measurement science needs that are critical to real-time process control. We organize our research findings to identify the correlations between process parameters, process signatures, and product quality. The intention of this report is to serve as a background reference and a go-to place for our work to identify the most suitable measurement methods and corresponding measurands for real-time control.
, Lane, B.
, Donmez, M.
, Feng, S.
, Moylan, S.
and Fesperman, R.
Measurement Science Needs for Real-time Control of Additive Manufacturing Powder Bed Fusion Processes, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.IR.8036
(Accessed October 17, 2021)