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• W4382938162 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 $$50.00,upmu $$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mn>50.00</mml:mn> <mml:mspace /> <mml:mi>μ</mml:mi> </mml:mrow> </mml:math> m with a classification accuracy of $$73.13pm 1.57%$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mn>73.13</mml:mn> <mml:mo>±</mml:mo> <mml:mn>1.57</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> This is the first work in the literature demonstrating in-situ localization of porosities as small as $$38.12,upmu m$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mn>38.12</mml:mn> <mml:mspace /> <mml:mi>μ</mml:mi> <mml:mi>m</mml:mi> </mml:mrow> </mml:math> 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." @default.
• W4382938162 created "2023-07-04" @default.
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• W4382938162 date "2023-07-03" @default.
• W4382938162 modified "2023-09-25" @default.
• W4382938162 title "In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach" @default.
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• W4382938162 doi "https://doi.org/10.1007/s10845-023-02170-9" @default.
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