Fundamental Measurements for Metal Additive Manufacturing

Summary

Critical parts for defense, aerospace, and medical applications must be formally qualified prior to use. Currently, extensive empirical testing consisting of thousands of individual tests, costing millions of dollars and necessitating several years to complete, is required to qualify parts built through additive manufacturing (AM) and minor changes to the process requires complete re-qualification. Additionally, complex surface topographies and features, internal defects, and anisotropic/location specific properties challenge current state of the art measurement techniques and testing protocols. All of this hinders the manufacturers’ and users’ ability to benefit from the key advantages that AM has to offer (i.e., complexity of design and reduced development time over traditional manufacturing methods). To address this need, the Fundamental Measurements for Metal Additive Manufacturing (FM2AM) project will develop the measurement science that creates robust measurements and testing to develop the correlations, enable qualification of AM parts, processes, and feedstocks by manufacturers, and reduce the need for complete re-qualification.

Description

Objective
The objective of this project is to instill confidence and aid adoption of AM as a viable technology for production in critical applications. To achieve this goal, a strong understanding of how to measure, characterize and qualify AM parts, processes, and feedstock materials is required.

Technical Idea
The program will develop measurement science solutions for pre-process, in-process, and post-process metrology, characterization, and inspection needs in metal-based AM. Through robust measurement methods and tools, as well as unambiguous measurement data representation, data analytics, and machine learning tools, the program aims to improve the understanding of AM process physics and implementation of process control to establish guidelines for new AM design rules and to enable rapid qualification of AM machines, processes and resulting parts.

• In the area of pre-process metrology, the program will focus on characterizing the precursor materials and machine performance. Understanding performance of precursor materials in both virgin and recycled states will enable optimum use of materials characterization techniques. Precursor materials testing and characterization will inform best practices for quality assurance. Test methods to characterize various aspects of machine performance, such as laser power distribution, will improve understanding of how sub-systems impact process and part variability.
• In the area of in-process metrology, the program will focus on real-time measurements of process signatures such as melt-pool temperatures and associated emissivity variation, powder layer characteristics, such as layer uniformity and density, material phase evolution, as well as in-situ non-destructive evaluation for detecting process-induced defects in real time. Reference and exemplar data sets will be generated and provided, through publicly accessible data base, to AM modeling community to validate and improve AM models.
• In the area of post-process metrology, the program will focus on developing and deploying test methods and protocols, standard test artifacts, exemplar data, data processing tools, and automation tools that create robust post-process measurements and non-destructive testing to enable qualification of AM parts by manufacturers.
• The program will also focus on developing algorithms, methods, and standard protocols for AM process control, and implement it with software and hardware tools for open control of AM systems to enable more flexible process optimization.
• To facilitate rapid qualification, the program will investigate new test methods and protocols, provide exemplar data, and establish requirements to reduce the cost and time needed for manufacturers to qualify metal AM machines and processes.
• In order to facilitate the effective and efficient curation, sharing, processing and use of measurement data and enable AM knowledge discovery for process improvement, the program will focus on developing and deploying models, methods and best practices for data management, data integration, and data fusion in additive manufacturing.

Finally, the advanced analytics and machine learning methods and tools will be applied to the curated measurement data to develop guidelines and provide decision-support (feed forward and feedback) for AM design and process planning to manage uncertainty and reduce the lead times in AM part fabrication. 
 

Research Plan
The scope of the FM2AM project is vast. Thus, there are many research tasks that will be performed simultaneously to successfully accomplish the goals set forth by the project. Equipment ranging from commercially available to novel and experimental, will be used to accomplish the following tasks:

• The relationship between flowability and spreadability, as well as the effect of powder layer density on part quality will be assessed experimentally. Commercial and experimental powder characterization equipment, the powder spreadability testbed developed at NIST, optical microscopy, and melt pool size characterizations will be used in the analysis to support correlation development and the advancement of feedstock characterization methods. Methodologies and results of this work will be used to develop correlations between these factors and will be disseminated to the community through research publications and standards development.
   
• Idealized experiments with simple conditions (e.g., bare plate, single tracks) and real process conditions will be performed to understand variability of melt pool geometry, define measurement methodologies that improve repeatability of measurements, and assess the quality of such information for engineering decision and process development.
   
• Test methods will be developed and executed to determine powder and process performance under denudation-like conditions using the new understandings developed to control denudation, and in collaboration with the Advanced Machines, Monitoring and Control (AMMC) project. These experiments will also be contrasted with feedstock characterization analyses to support feedstock qualification metrics.
   
• Spatial and temporal resolution of the reflected laser power metrology equipment developed through other MSAM projects, and in collaboration with the AMMC and MAMMV projects, will be refined and well characterized to provide cross-comparisons with process monitoring data and strengthen our understanding of this phenomena.
   
• Defect artifacts that are representative of the defects seen in LPBF will be developed. Techniques such as AM, FIB, Laser micromachining, and projection photolithography have been demonstrated, and maskless lithography, and nanoindentation will be further investigated for different substrate materials and defect characteristics. Different assembly techniques and reference measurement methods for created features will be investigated. The findings of the demonstration studies will be shared to community for guidance (i.e., via journal publications, standards development), and allowing industries to identify the best techniques for their applications. Prototype artifacts will be developed, and demonstration studies on POD, probability of sizing (POS), and/or automated detection algorithms will be performed using the artifacts.
   
• Benchmark XCT defect data sets will be developed and shared to community to evaluate various defect detection algorithms. Data sets will be prepared through XCT simulation and from measurements of physical artifacts under development. A computational framework is being developed to generate realistic simulation data. Various detection algorithms including those based on AI/ML will be investigated using different evaluation metrics to help with designing challenge problems.
   
• XCT; optical and tactile surface, form, and coordinate metrology; and other non-destructive testing (NDT) systems will be used to develop more detailed and quantitative characterization of part dimensions, form, surface finish, defect morphology, and defect locations. Test artifacts based on the unique dimensional characteristics of AM parts will be developed to better understand how part and surface complexity affect a metrological system’s performance.
   
• This work will continue the case study in the development and realization of surface topography measurands that is already underway. Experiments and analysis will be performed to better understand the repeatability of various measurement relevant to the AM process. This will also provide a basis for understanding how these measurands vary from machine-to-machine, operator-to-operator, etc.

Where and to the extent possible, the output of the above tasks will be integrated into the holistic fabrication and qualification process. Successful methodologies will be disseminated to the wider AM community and utilized as a model for incorporating additional outputs. Challenges will be documented and studied to identify future research tasks. Finally, this project will support the development of measurement science to support equivalence-based qualification method and work closely with other AM projects in the MSAM program to develop use cases/guidelines for model-based qualification.