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Comparative evaluation of parametric models of porosity in laser powder bed fusion

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Abstract

Porosity is a critical defect in laser powder bed fusion that limits the adoption of this technology. The variations in process parameters affect the level of porosity in additively manufactured parts. Due to the complex multiphysics of the laser powder bed fusion process, surrogate models can be used to predict the amount of porosity from the process parameters. Regression and machine learning approaches have been used for the porosity prediction. However, these models are developed for certain materials. This study compares different surrogate models for correlating the amount of porosity and the process parameters in combination with proposed dimensionless numbers that are dependent to both the process parameters and powder material properties. Regression, support vector machine, and neural networks models are trained using lack of fusion porosity synthetic data for three different materials. The results show that the support vector machine and the multi-layer neural network models that use process parameters and the dimensionless numbers as the independent variables can effectively predict the amount of porosity regardless of materials.

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Availability of data and material

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code that supports the findings of this study is available from the corresponding author upon reasonable request.

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Funding

The support from the San Diego State University is gratefully acknowledged.

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  1. Department of Mechanical Engineering, San Diego State University, San Diego, CA, 92182, USA

    Luis Waldo Escalona-Galvis & John S. Kang

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  1. Luis Waldo Escalona-Galvis
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Correspondence to John S. Kang.

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Escalona-Galvis, L.W., Kang, J.S. Comparative evaluation of parametric models of porosity in laser powder bed fusion. Int J Adv Manuf Technol 122, 3693–3701 (2022). https://doi.org/10.1007/s00170-022-10129-y

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