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Part quality in additive manufacturing (AM) is highly variable due inadequate dimensional tolerances, surface roughness, and defects, thereby limiting its broad acceptance. This variability could be minimized through process control but there is a lack of adequate process measurement tools available today. In particular, methods for measuring the melt-pool temperature distribution, part dimensions, microstructure, and surface finish in real time do not exist. As a result, manufacturers adjust process parameters based on heuristics and previous fabrication runs, yielding limited improvement in part quality and requiring many build runs for convergence. This project will focus on the fundamental measurement science necessary to make process measurement and control possible in additive manufacturing. Specifically, methods for traceable thermal and dimensional metrology will be developed and then applied to real-time closed-loop control of additive manufacturing processes. Accessible methods for process measurement and control will allow industry to make higher quality parts cheaper and faster and accelerate the proliferation of additive manufacturing parts into high-performance applications in the aerospace, defense, and biomedical industries. This work is well aligned with NIST's fundamental measurement science mission that has a strong impact on U.S. industry.
Objective - Develop process metrology, in-process sensing methods, and real-time process control approaches to maximize part quality and production throughput in Additive Manufacturing (AM).
What is the new technical idea? Additively manufactured parts can have many quality issues, such as undesired porosity, delamination of layers, shrinkage, poor surface finish, dimensional and form errors, as well as thermal stresses and distortion. The part quality is primarily affected by parameter settings in an AM process. Today, most process parameters setting is done by trial-and-error. This is time consuming, costly, highly subjective, and machine- and material-specific. Due to unknown cause-and-effect relationships between the manufacturing process parameter settings and the process characteristics, the resulting part quality is highly variable creating significant limitation to the widespread adoption of AM technology.
In-process sensing and real-time control of AM processes are identified as the main enablers to minimize variations in AM processes and resulting product quality and production throughput (i). This project will focus on the fundamental measurement science necessary to make in-process measurement and control possible in AM. Specifically, methods for traceable thermal and dimensional metrology will be developed and then applied to real-time closed-loop control of AM processes.
What is the research plan? In metal-based AM, significant portion of the part quality issues is attributed to the melting and cooling of metal powders. There is a lack of adequate level of understanding of the process physics to be able to characterize such melting and cooling processes. Innovations in process metrology are needed to provide traceable and quantitative data for validating process models, calibrating in-process sensors, and determining optimal process conditions. In the area of process metrology for validating process models, this project will collaborate with the AM Qualification project in developing traceable infrared thermography and spectroscopy methods for measuring the temperature distribution of the melt pool in powder bed fusion processes. Validated physics-based process models will be used to develop reduced-order analytical models for use in development of real-time control algorithms.
Real-time control of additive processes will require robust in-process sensing methods and tools. In-process sensors differ from metrology instruments in that they typically must be compact and affordable for permanent installation in AM machines. However, accuracy and traceability requirements are generally less demanding than for process metrology for validating the process models. The most important in-process sensing capabilities are related to melt pool temperature distribution and geometry, the dimensions of fabricated layers and manufactured parts, and the detection of defects. This project will establish the metrics and test methods for assessing the performance of such measurement tools. High-speed image-based methods for detecting defects during processing (including cracks, delaminations, and voids) will be developed.
In the process control area, two groups of process control approaches will be pursued: open and closed-loop control. Using high-fidelity process models and post-process feedback, iterative open-loop control algorithms will be developed. This approach will be implemented on commercial AM machines. In parallel, closed-loop control algorithms will be developed utilizing melt pool temperature and size, layer-by-layer part geometry, and defect characteristics as feedback. Along with these control algorithms, metrics for performance characterization of such process control systems will be developed.
Manufacturers of powder bed fusion machines generally do not allow modifications by end-users and are reluctant to provide open access to software and control systems. This creates barriers for the development and implementation of real-time process control. Therefore, this project will develop a bench-top open AM platform to test and demonstrate the in-process measurement and control methods. This development will be leveraged by the efforts in PML developing a surface temperature and emittance measurement system (TEMPS). Such a bench-top platform will enable us to directly observe melting and solidification of metal powders, integrate process metrology tools, implement software interfaces and data acquisition for process measurements as well as test the control algorithms.
1Measurement 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:
Alkan Donmez, Project Leader
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