Porous Metal Properties Analysis: A Machine Learning Approach

Avalos-Gauna, Edgar; Zhao, Yuyuan; Palafox, Leon; Ortiz-Monasterio-Martínez, Pedro

doi:10.1007/s11837-021-04695-x

Cited by 4 publications

(2 citation statements)

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“…Avalos‐Gauna et al 47 predict permeability and Forchheimers coefficient of lost carbonate sintering open‐cell porous metal based on porosity, pore size, and coordination number by using linear and polynomial regression, random forests, and ANN. The best results in predicting the Forchheimer coefficient were obtained by the random forest, and the best results to predict permeability were achieved by using an ANN.…”

Section: Overview On the Fatigue Of Pm Steels And Machine Learning Ap...mentioning

confidence: 99%

“…Cherian et al 45 used a ANN to predict the sintered density, compaction pressure, sintering temperature, sintering atmosphere, and percent carbon and copper additions for specified tensile strength, elongation, and hardness. An example application of an ANN for the estimation of the fatigue strength is shown in Hajeck et al 46 Avalos-Gauna et al 47 predict permeability and Forchheimers coefficient of lost carbonate sintering open-cell porous metal based on porosity, pore size, and coordination number by using linear and polynomial regression, random forests, and ANN. The best results in predicting the Forchheimer coefficient were obtained by the random forest, and the best results to predict permeability were achieved by using an ANN.…”

Section: Literature On Machine Learning In Connection To Fatiguementioning

confidence: 99%

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New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning

Leininger

Reissner

Baumgartner

2022

Fatigue Fract Eng Mat Struct

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Up to now, no consistent fatigue assessment approach of powder metallurgy (PM) components is available. For some materials and for some parameters, such as the density, a relationship to the fatigue strength is known; however, for other materials, such relationships are unknown. Based on an extensive data set with 828 test series, the present work addresses this problem by conceiving and applying five machine learning (ML)-based approaches to increase the accuracy of the prediction of the fatigue life as well as to predict the scatter of unknown data as precisely as possible. With the elaborated procedure, on the one hand, a scatter range of T S ¼ 1 : 1:40 can be achieved on completely unknown data. On the other hand, by using a newly defined loss function, the standard deviation of unknown data can be predicted very accurately. The findings provide the basis for further research on cost and efficiency optimized design of PM components through better estimation of fatigue life.

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Section: Overview On the Fatigue Of Pm Steels And Machine Learning Ap...mentioning

confidence: 99%

Section: Literature On Machine Learning In Connection To Fatiguementioning

confidence: 99%