Towards rapid qualification of powder-bed laser additively manufactured parts
Source abstract
Abstract
Qualification of aerospace components is a long and costly process involving material properties, material specifications, manufacturing process, and design among others. Reducing qualification time and cost while maintaining safety offers a large economic advantage and enables faster response to the market demands. In 2012, DARPA established the Open Manufacturing program, a project to develop an integrated computational materials engineering (ICME) framework aimed at rapid qualification. Rapid qualification requires the integration of several technologies: materials, process, design, models, monitoring and control, non-destructive evaluation (NDE), testing, among others. A probabilistic design approach is adopted in the rapid qualification process to enable the integration of these technologies into a single risk-based function to optimize the design process. This approach directs the efforts to those areas that play the most important roles, potentially reducing specimen testing that will be required to develop material databases and design limits. New tests also will be required to validate and verify the ICME framework and develop a better understanding of the processing-microstructure-property relation and associated variability of the processing conditions. The probabilistic design approach is demonstrated for the rapid qualification of an actual aircraft engine component constructed via the powder-bed additive manufacturing process. This paper summarizes the probabilistic rapid qualification design approach and its application to this novel manufacturing process with the goal of reducing the overall qualification process time by 40 % and qualification process cost by 20 %.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Manufacturing Process and Optimization · Technology Assessment and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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