Atwya, M. orcid.org/0000-0003-2699-1415 and Panoutsos, G. orcid.org/0000-0002-7395-8418 (2023) In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach. Journal of Intelligent Manufacturing, 35 (6). pp. 2719-2742. ISSN 0956-5515
Abstract
Numerous efforts in the additive manufacturing literature have been made toward in-situ defect prediction for process control and optimization. However, the current work in the literature is limited by the need for multi-sensory data in appropriate resolution and scale to capture defects reliably and the need for systematic experimental and data-driven modeling validation to prove utility. For the first time in literature, we propose a data-driven neural network framework capable of in-situ micro-porosity localization for laser powder bed fusion via exclusively within hatch strip of sensory data, as opposed to a three-dimensional neighborhood of sensory data. We further propose using prior-guided neural networks to utilize the often-abundant nominal data in the form of a prior loss, enabling the machine learning structure to comply more with process physics. The proposed methods are validated via rigorous experimental data sets of high-strength aluminum A205 parts, repeated k-fold cross-validation, and prior-guided validation. Using exclusively within hatch stripe data, we detect and localize porosity with a spherical equivalent diameter (SED) smaller than 50.00μm with a classification accuracy of 73.13 +- 1.57%. This is the first work in the literature demonstrating in-situ localization of porosities as small as 38.12μm SED and is more than a five-fold improvement on the smallest SED porosity localization via spectral emissions sensory data in the literature. In-situ localizing micro-porosity using exclusively within hatch-stripe data is a significant step towards within-layer defect mitigation, advanced process feedback control, and compliance with the reliability certification requirements of industries such as the aerospace industry.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/. |
Keywords: | Prior-guided neural network; Machine learning; Structure optimization; Metal laser powder bed fusion; Porosity |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/P006566/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Nov 2023 15:24 |
Last Modified: | 08 Jul 2024 12:03 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1007/s10845-023-02170-9 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205610 |