Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Conditional Generative Adversarial for in-Situ Layerwise AM

Version 1 : Received: 24 January 2021 / Approved: 25 January 2021 / Online: 25 January 2021 (15:55:32 CET)

How to cite: Christian, G.; Edel, A.; Brandon, M. Conditional Generative Adversarial for in-Situ Layerwise AM. Preprints 2021, 2021010519. https://doi.org/10.20944/preprints202101.0519.v1 Christian, G.; Edel, A.; Brandon, M. Conditional Generative Adversarial for in-Situ Layerwise AM. Preprints 2021, 2021010519. 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.

Keywords

machine learning; additive manufacturing; conditional generative adversarial network; in-situ monitoring

Subject

Engineering, Automotive Engineering

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