To develop and deploy 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.
What is the Problem?
To instill confidence and aid adoption of AM as a viable technology for production in critical applications, a strong understanding of how to measure, characterize, and qualify parts is required. The complexity of additive manufacturing (AM) has created new and unique measurement challenges that must be addressed. (1) Complex surface topography and features, such as lattice structures, internal geometries, and topology optimized parts, challenge the current state of the art for dimensional characterization (e.g., geometric dimensions and accuracy, form, surface finish). (2) Internal defects (e.g., pores) are detrimental to the performance of AM parts; thus, the detection and elimination of these defects is critical for qualification. (3) Mechanical property measurements of AM parts are complicated by residual stresses, intentional or unintentional gradients, metastable phases, and a high degree of anisotropy at the micro and macroscale that requires innovative testing protocols. All three of these measurement categories may also be affected by post-process treatments to the part (e.g., heat treatment, hot isostatic pressing, machining, chemical/mechanical polishing, etc.).
What is the Technical Idea?
To address these challenges, the AM Part Qualification project will focus on the measurements, methods, and metrological systems required to create robust post-process measurements, develop a strong understanding of mechanical performance, and qualify AM parts. 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 will be developed to enable qualification of AM parts by manufacturers.
The research and deliverables provided by this project will help address the following gaps identified by the America Makes and ANSI Additive Manufacturing Standardization Collaborative (AMSC) Roadmap v2.0: D8 – Machine Input and Capability Report; D14 – Design of Test Coupons; D16 – Verifying Functionally Graded Materials; D18 – New Dimensioning and Tolerancing Requirements; D28 – Specification of Surface Finish; D26: Design for Measurement of AM features/Verifying the Designs of Features; P1 – Post-processing Qualification and Production Builds; P2 – Heat Treatment (HT)-Metals; P3 – Hot Isostatic Pressing; P4 – Surface Finish; FMP1 – Material Properties; FMP4 – Design Allowables; FMP5 – Microstructure; QC7 – Protocols for Image Accuracy; QC8 – Phantoms; NDE1 – Terminology for the Identification of AM Flaws Detectable by NDE Methods; NDE2 – Standard for the Design and Manufacture of Artifacts or Phantoms Appropriate for Demonstrating NDE Capability; NDE3 – Standard Guide for the Application of NDE to Objects Produced by AM Processes; NDE4 – Dimensional Metrology of Internal Features; NDE 5 – Data Fusion; NDE 8 – Acceptance Criteria for Fracture Critical AM Parts;
What is the Research Plan?
There are four main areas on which the project will focus efforts: measurement of complex geometries, measurement and characterization of surface topography, defects, and mechanical performance. X-ray computed tomography (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 and defects will be developed to better understand how part and surface complexity affect a metrological system’s performance and perform probability of detection (POD) studies in various NDT systems. Advanced testing techniques will be used to address the need for high throughput, location specific mechanical property measurements. Synchrotron X-ray and neutron diffraction will be used to characterize residual stresses at various length scales of AM materials and determine the resulting implications on part qualification. Exemplar data from these measurements will also be made available to encourage collaboration among government, industry, and academic researchers and allow experts in the precision metrology community to explore new and innovative methods of analyzing measurement data and correlating it to part features and quality. Advancements in these areas will lead to a stronger understanding of part and defect characterization, help determine methods to isolate or seed key features and defects in a design of experiments for mechanical performance testing, and create a foundation for the qualification of AM parts.
Additionally, the AM Part Qualification project will work closely with standards developing organizations (SDOs), industry/government/academic researchers, and other Measurement Science for Additive Manufacturing (MSAM) projects to mutually benefit from knowledge gained, including: the Real-time Monitoring of AM project as their high degree of control over the process will help isolate process variables for developing process-structure relationships and identification of defect morphology, defect generation, and process-structure relationships will provide signatures to monitor for in situ; the AM Machine and Process Qualification project as identification of relationships between process variables and resultant part quality will help generate tolerances for machine performance monitoring and qualification; the AM Data Integration and Management project as data fusion will be required to merge multiple sources of measurement for richer information about part quality than the individual sources alone; and the Design and Data Analytics project as concepts learned through the AM Part Qualification project will help define challenges for product definition, GD&T, and design rules, and will benefit from investigations of data analytics, AI, and machine learning for AM.