Tensile behaviour prediction of laser powder bed fusion with engineered process parameters using a feedforward neural network

Zhihui Meng; Sergio Corbera Caraballo; Rosalía Rementería Fernández; Alberto Rodríguez Alvárez; Lucía Fernández Rodríguez; Laura del Río Fernández; Rafael Barea del Cerro

doi:10.1117/12.3050287

18 October 2024 Tensile behaviour prediction of laser powder bed fusion with engineered process parameters using a feedforward neural network

Zhihui Meng, Sergio Corbera Caraballo, Rosalía Rementería Fernández, Alberto Rodríguez Alvárez, Lucía Fernández Rodríguez, Laura del Río Fernández, Rafael Barea del Cerro

Author Affiliations +

Proceedings Volume 13285, International Conference on Precision Engineering and Mechanical Manufacturing (PEMM 2024); 1328507 (2024) https://doi.org/10.1117/12.3050287
Event: Second International Conference on Precision Engineering and Mechanical Manufacturing (PEMM 2024), 2024, Incheon, Korea, Republic of

Abstract

In the past few years, there are an increasing number of studies applying machine learning algorithms to laser powder bed fusion additive manufacturing process-property problems due to the complex physics behind. Most of the experiments are designed for human learning rather than for machine learning. Moreover, the authors identified an opportunity to utilise the massive number of studies regarding the physical phenomena in the process to predict the tensile properties. In this paper, a randomised experimental dataset is designed, and the resulting tensile properties demonstrate large variations. Despite the process parameters for laser powder bed fusion process, several engineered process parameters from literature have been fed to a deep feedforward neural network to predict the tensile behaviours. These engineered process parameters contribute to the high precision of predictions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhihui Meng, Sergio Corbera Caraballo, Rosalía Rementería Fernández, Alberto Rodríguez Alvárez, Lucía Fernández Rodríguez, Laura del Río Fernández, and Rafael Barea del Cerro "Tensile behaviour prediction of laser powder bed fusion with engineered process parameters using a feedforward neural network", Proc. SPIE 13285, International Conference on Precision Engineering and Mechanical Manufacturing (PEMM 2024), 1328507 (18 October 2024); https://doi.org/10.1117/12.3050287

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