Objective: Develop test methods and protocols, provide reference data, and establish requirements to reduce the cost and time to qualify AM materials, processes, and parts.
Technical Idea: Qualification of AM materials, processes, and parts is critical to enabling the AM industry to crack into the defense, aerospace, and medical industries. There are generally three different paths to qualification: 1) statistical-based qualification rooted in extensive (and costly) empirical testing, 2) equivalence-based qualification achieved through moderate testing to demonstrate a new material or process is equivalent to a previously qualified material or process, and 3) model-based qualification where a material’s or process’ performance is demonstrated in a computer model and verified with minimal testing. Currently no AM processes or materials are qualified for critical defense or aerospace applications. Non-critical AM parts have been qualified using statistical approaches, but the high cost in time and money encourage companies to keep the resulting data proprietary. NIST will focus on developing the measurement science to reduce the time and cost related to qualification. This will entail test methods and protocols to distribute the cost of statistical approaches, and reference data to support equivalence-based qualification and model-based qualification. These will enable AM users to qualify materials and processes without the high cost required by building and empirically testing hundreds or thousands of AM parts.
NIST is a non-regulatory body, therefore actual qualifications and their specific protocols will be left to regulatory bodies (e.g., the Federal Aviation Administration). However, NIST will deliver measurement science that will establish the foundation for qualification of materials, processes, and parts used in AM at reduced cost. This will be accomplished by developing novel test methods and protocols, leveraging work in other EL AM projects, and collaborating with key industrial and academic researchers.
The high cost of statistical-based qualification is mainly the result of the time and money required to complete the extremely large number of tests. It is likely impossible to achieve qualification without some amount of testing, especially in the case of getting the first AM material or process qualified. However, it is possible to distribute the burden of testing by improving the AM industrial commons. This project will work closely with AM Materials Characterization project to make testing of AM materials more accessible. These projects will define protocols for round robin testing and produce a publicly available materials database to allow multiple contributors (both large and small) to add trusted data to the existing knowledge base.
Research Plan: A prerequisite to model-based qualification is a validated process model. This project will partner with industrial and academic researchers developing high-fidelity multi-physics process models to provide them with trusted data that can be used as improved model inputs and for model validation. Understanding the temperatures involved in the melting and re-solidification of metal AM processes is of primary importance. As such, initial efforts will be in delivering improved emittance and temperature measurements with known measurement uncertainties that modelers can use as a basis for validation. Temperature models will feed material models that predict AM material and part performance with respect to microstructure and residual stress. This project will deliver data that can be used as validation in these areas as well.
The long range goal of part qualification is the concept of “qualify as you go.” This concept uses pre-process, in process, and post process measurements to demonstrate that a part will perform to specifications. This project will work closely with the AM Process Control project to develop improved measurement techniques to better characterize AM processes and resulting parts. Defining the key process parameters that most influence part performance will lead to pre-process tests to characterize machine performance will demonstrate that the machine is performing as expected. Establishing critical geometries, both external and internal, that need precision measurement and how AM users measure them is an important first step before developing post-process dimensional metrology techniques.