Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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 (doi: 10.20944/preprints202101.0519.v1). Christian, G.; Edel, A.; Brandon, M. Conditional Generative Adversarial for in-Situ Layerwise AM. Preprints 2021, 2021010519 (doi: 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.

Subject Areas

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

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.