Materials and processes used to produce critical components for defense, aerospace, and medical applications must first be formally qualified. Extensive empirical testing to fully qualify a material often requires many thousands of individual tests, costing millions of dollars and 5 to 15 years to complete.1 Further, a minor change in the process requires complete re-qualification. The variety of AM processes available to users and the variety of process variables used to produce an individual part make statistical-based qualification through empirical testing particularly burdensome.2 Currently no AM processes or materials are qualified for critical defense or aerospace applications. Non-critical AM materials and processes have been qualified using empirical testing with fewer tests, but the cost and time remain high, encouraging companies to keep the resulting data proprietary. However, there are generally three different paths to qualification: statistical-based qualification rooted in extensive empirical testing, equivalence-based qualification achieved through moderate testing to demonstrate a new material or process is equivalent to a previously qualified material or process, and model-based qualification where a material’s or process’ performance is demonstrated in a computer model and verified with minimal testing. Developing the measurement science to support equivalence-based qualification and model-based qualification will enable AM users to qualify materials and processes without the high cost and time required by statistical-based qualification. NIST’s excellence in measurement science and its standing as a neutral third party with a broad public forum make it the ideal place to develop the test methods and protocols, provide the reference data, and establish the minimum requirements needed to achieve more rapid qualification. This measurement science will not only ease the qualification process in aerospace and defense applications, but will also lead to a better understanding of AM and more confidence in AM products used in all industries.
By FY18, develop test methods and protocols, provide reference data, and establish requirements to reduce the cost and time to qualify AM materials, processes, and parts.
What is the new technical idea?
There are generally three different paths to qualification: statistical-based qualification rooted in extensive (and costly) empirical testing, equivalence-based qualification achieved through moderate testing to demonstrate a new material or process is equivalent to a previously qualified material or process, and 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 materials and processes have been qualified using statistical approaches, but the high cost in time and money encourage companies to keep the resulting data proprietary. This project will focus on developing the measurement science to support equivalence-based qualification and model-based qualification to enable AM users to qualify materials and processes without the high cost required by statistical-based qualification.
What is the research plan?
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 publically available materials database to allow multiple contributors (both large and small) to add trusted data to the existing knowledge base.
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 the laser powder bed fusion process 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.
1Brice, C. (2011), “Unintended Consequences: How Qualification Constrains Innovation,” Proceedings of the 1st World Congress on Integrated Computational Materials Engineering (ICME), (eds. J. Allison, P. Collins, and G. Spanos), John Wiley & Sons Inc., Hoboken, NJ, USA
2Measurement Science Roadmap for Additive Manufacturing: http://events.energetics.com/NIST-AdditiveMfgWorkshop/pdfs/NISTAdd_Mfg_Report_FINAL.pdf
Start Date:October 1, 2013
Lead Organizational Unit:el
Related Programs and Projects:
Shawn Moylan, Project Leader
301 975 4352 Telephone
100 Bureau Drive, M/S 8230