In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach

Mohamed Atwya () and George Panoutsos ()
Additional contact information
Mohamed Atwya: The University of Sheffield
George Panoutsos: The University of Sheffield

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 6, No 15, 2719-2742

Abstract: 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 $$50.00\,\upmu $$ 50.00 μ m with a classification accuracy of $$73.13\pm 1.57\%$$ 73.13 ± 1.57 % This is the first work in the literature demonstrating in-situ localization of porosities as small as $$38.12\,\upmu m$$ 38.12 μ m 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.

Keywords: Prior-guided neural network; Machine learning; Structure optimization; Metal laser powder bed fusion; Porosity (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02170-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:6:d:10.1007_s10845-023-02170-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02170-9

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().