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An example of a feature-based qualification test process where multiple samples are printed in a single build with varying process parameters to capture different additively built characteristics and feature geometries.
This project seeks to develop and demonstrate an additive manufacturing part qualification process methodology of feature-based build qualifications (FBQs) using a directed energy deposition (DED) process to reduce the time and cost of part qualification.
It can take several years to go from concept to production for components produced by additive manufacturing (AM). The flexibility of the AM process allows for build customization including build strategy, process conditions, etc. This leads to long iterative evaluation cycles—build strategy, printing parts, post processing, dimensional inspection, material performance, and functional performance—which greatly increases the time and cost of substantiating the process and component.
The objective of this project is to improve the speed and confidence of directed energy deposition (DED) process qualification by identifying application space, process technology, and materials; developing and validating feature-based build qualifications (FBQ) by cataloging features, build process parameters, additive characteristics, and material property relationships; deploying the FBQ catalog for design engineering; and process qualification.
GE Global Research Center (GEGRC) is focusing on developing FBQ by first identifying part-based critical features (including engineering and performance requirements), testing protocols, and printing specimens, thus establishing additive characteristics and performance measurements. In addition, probabilistic modeling and efficient sampling are being developed for a performance catalog based on FBQ methodology.
GE Bayesian Hybrid Modeling framework (GEBHM) in conjunction with Intelligent Design Analysis of Computer Experiments (IDACE) is being used to build predictive models of each feature for the relationship between process parameters, additive build characteristics, and material performance. Implementation efforts include defining the application space, process technology and materials; generating hybrid probabilistic models relating feature performance to process; and deploying hybrid models to the engineering community. Training development and pilots are also planned.