LPBF Right the First Time—the Right Mix Between Modeling and Experiments
Source abstract
Abstract
Laser powder bed fusion (LPBF) is an additive manufacturing process with many adjustable input parameters that directly affect manufacturability and quality of the final product. The selection of the optimal input parameters makes the process qualification and part certification a costly and time-consuming task if performed using the traditional sequential and empirical approach. Within the scope of the DARPA open manufacturing program, a rapid qualification framework is developed that relies on parallel multi-physics modeling and experimental efforts for verification and validation of the process input parameters during process development and material characterization. Product manufacturability is tested a priori via modeling and in-process monitoring is deployed to ensure input parameters are rapidly screened, and an optimal process window is selected. Process consistency and repeatability is further ensured through process characterization, process qualification, and via quantitative analysis of digital In-Process Quality Metrics™ (IPQM®s). This paper discusses the rapid qualification methodology, model validation, and the application of the framework towards manufacturing of a challenging part defined by AFRL. The combination of numerical predictions, experimental refinement, and in-process monitoring delivered the first print right at first trial. Distortions are within predictions, geometric accuracy is within expectations, and quantitative metallurgical analysis shows dense as-built material with properties expected to fulfill performance requirements. In-process monitoring results provide a quantitative, digital Quality Signature™ or Digital Quality Record™ of process consistency and product quality.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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