Part quality in additive manufacturing (AM) is highly variable due to 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, scan track, and layer characteristics, and making well-informed control strategies based on these measurements are limited and underdeveloped. 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 metals additive manufacturing. Specifically, methods will be developed for in-situ monitoring of process signatures at the melt pool or layer-wise scale and will be analyzed with relation to process physics and final part quality. In addition, calibration and characterization techniques for the monitoring tools will be designed for potential standardization. Ultimately, intelligent control decisions will be formulated based on in-situ monitoring results, and strategically applied at the per-layer or real-time rate. Accessible methods for process measurement and control will allow industry to build, qualify, and certify parts with greater throughput and accelerate the proliferation of additive manufacturing parts into high-performance applications in the aerospace, defense, and biomedical industries.
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).
Technical Idea: Additively manufactured metal parts can have many quality issues, such as undesired pores or cracks, undesired deformation resulting in dimensional and form errors, poor surface finish, as well as more difficult to detect qualities such as internal residual stresses and inhomogeneous and anisotropic microstructure. The part quality is primarily affected by parameter settings in an AM process, though the dynamic nature of the process physics also induces stochastic variability. 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, monitoring and real-time control of AM processes are identified in the NIST Measurement Science Roadmap for Additive Manufacturing as the main enablers to minimize variations in AM processes and resulting product quality and production throughput. This project will focus on the fundamental measurement science necessary to make in-process measurement and control possible in AM. Specifically, methods for in-situ measurement of thermal, spatial, and temporal process signatures will be developed, their relationships to process physics and part qualities will be identified, and well-informed process control strategies and algorithms based on these relationships will be developed, tested, and disseminated.
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. Therefore, this project will research the fundamental physics of laser-induced melting and solidification of metal powders, with emphasis on studying mechanisms that drive defect and flaw formation during the AM process. In addition, temperature, thermal gradient, and cooling rate of the melt pool and heat affected zone (HAZ) are principle factors that drive process physics.
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.
Research Plan: 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 laser 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.
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. 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 an open AM platform and supporting facility called the Additive Manufacturing Metrology Testbed (AMMT) to test and demonstrate the in-process measurement and control methods. This system development is co-led by the efforts by NIST Physical Measurement Laboratory (PML) to develop a traceable surface temperature and emittance measurement system called Temperature and Emissivity of Melts, Powders, and Solids (TEMPS). Such a system will enable us to directly observe melting and solidification of metal powders, integrate in-situ process metrology tools, implement software interfaces and data acquisition for process measurements as well as test the open and closed-loop control algorithms.