Model-Based Production Operational Control for Metal Additive Manufacturing

Published

September 15, 2024

Author(s)

Duncan Gibbons, Paul Witherell

Abstract

Metal additive manufacturing processes are influenced by many variables that affect the produced material to varying degrees. This wide range of variables and the complex fusion mechanisms inherent to these processes often result in a manufacturing process with poor repeatability and reproducibility. This is a qualification and certification concern for critical industries. This research aims to investigate the application of manufacturing reference architectures and models for metal additive manufacturing production definition and control. Such models aid qualification activities by defining critical to quality information and controlling production operations. A systems engineering methodology is utilized to design and develop a reference model for metal additive manufacturing operations. A case study is performed to demonstrate how the developed reference model can be leveraged to define requirements, define operations per STEP-NC, and generate machine-readable process control files. This demonstration illustrates how critical to quality data is captured and managed within the system model for compliance purposes. The proposed reference model provides an architecture for developing and implementing operational digital twins.

Proceedings Title

Proceedings of the Thirty-Fifth Annual International Solid Freeform Fabrication Symposium

Conference Dates

August 11-14, 2024

Conference Location

Austin, TX, US

Conference Title

The Thirty-Fifth Annual International Solid Freeform Fabrication Symposium

Pub Type

Conferences

Download Paper

Local Download

Keywords

Additive Manufacturing, Operational Control, Model-Based Systems Engineering, Metal LPBF

Citation

Gibbons, D. and Witherell, P. (2024), Model-Based Production Operational Control for Metal Additive Manufacturing, Proceedings of the Thirty-Fifth Annual International Solid Freeform Fabrication Symposium, Austin, TX, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958335 (Accessed October 1, 2025)