Christian, G.; Edel, A.; Brandon, M. Conditional Generative Adversarial for in-Situ Layerwise AM. Preprints2021, 2021010519. https://doi.org/10.20944/preprints202101.0519.v1
APA Style
Christian, G., Edel, A., & Brandon, M. (2021). Conditional Generative Adversarial for in-Situ Layerwise AM. Preprints. https://doi.org/10.20944/preprints202101.0519.v1
Chicago/Turabian Style
Christian, G., Arrietab Edel and McWilliams Brandon. 2021 "Conditional Generative Adversarial for in-Situ Layerwise AM" Preprints. https://doi.org/10.20944/preprints202101.0519.v1
Abstract
Conditional generative adversarial networks (CGANs) learn a mapping from conditional input to observed image and perform tasks in image generation, manipulation and translation. In-situ monitoring uses sensors to obtain real-time information of additive manufacturing (AM) processes that relate to process stability and part quality. Understanding the correlations between process inputs and in-situ process signatures through machine learning can enable experimental-driven predictions of future process inputs. In this research, in-situ data obtained during a metallic powder bed fusion AM process is mapped with a CGAN. A single build of two turbine blades is monitored using EOSTATE Exposure OT, a near-infrared optical tomography system of the EOS M290 system. Layerwise images generated from the in-situ monitoring system were paired with a conditional image that labeled the specimen cross-section, laser-scan stripe overlap and z-distance to part surfaces. A CGAN was trained using the turbine blade data set and employed to generate new in-situ layerwise images for unseen conditional inputs.
Copyright:
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