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Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting

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Abstract

To find a robust combination of selective laser melting (SLM) process parameters to achieve the highest relative density of 3D printed parts, predicting the relative density of 316L stainless steel 3D printed parts was studied using a set of machine learning algorithms. The SLM process brings about the possibility to process metal powders and built complex geometries. However, this technology’s applicability is limited due to the inherent anisotropy of the layered manufacturing process, which generates porosity between adjacent layers, accelerating wear of the built parts when in service. To reduce interlayer porosity, the selection of SLM process parameters has to be properly optimized. The relative density of these manufactured objects is affected by porosity and is a function of process parameters, rendering it a challenging optimization task to solve. In this work, seven supervised machine learning regressors (i.e., support vector machine, decision tree, random forest, gradient boosting, Gaussian process, K-nearest neighbors, multi-layer perceptron) were trained to predict the relative density of 316L stainless steel samples produced by the SLM process. For this purpose, a total of 112 data sets were assembled from a deep literature review, and 5-fold cross-validation was applied to assess the regressor error. The accuracy of the predictions was evaluated by defining an index of merit, i.e., the norm of a vector whose components are the statistical metrics: root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). From this index of merit, it is established that the use of gradient boosting regressor shows the highest accuracy, followed by multi-layer perceptron and random forest regressor.

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Data availability

Source files are available in GitHub (https://github.com/GermanOmar/SLM-ML).

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Acknowledgments

The main and corresponding author would like to acknowledge SENESCYT grant no. ARSEQ-BEC-000329-2017, the Research Center for Nanotechnology and Advanced Materials (CIEN-UC) and ANID FONDECYT grant no. 1201068 project for making this publication possible.

Funding

This study is financially supported by the SENESCYT grant no. ARSEQ-BEC-000329-2017 and ANID FONDECYT grant no. 1201068 project.

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Authors and Affiliations

  1. Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna, 4860, Macul, Santiago, Chile

    Germán Omar Barrionuevo, Jorge Andrés Ramos-Grez & Magdalena Walczak

  2. Research Center for Nanotechnology and Advanced Materials (CIEN-UC), Av. Vicuña Mackenna 4860, Macul, Santiago, Chile

    Jorge Andrés Ramos-Grez & Magdalena Walczak

  3. Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei, Taiwan

    Carlos Andrés Betancourt

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  1. Germán Omar Barrionuevo
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  2. Jorge Andrés Ramos-Grez
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  3. Magdalena Walczak
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  4. Carlos Andrés Betancourt
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Contributions

Germán Omar Barrionuevo: paper original idea, literature review and data collection, selection and implementation of machine learning algorithms, data processing, plot generation, and manuscript writing and funding.

Jorge Andrés Ramos Grez: paper original conceptualization, close supervision and guidance during research process, index of merit formulation, critical advice, and manuscript proofread and funding.

Magdalena Walczak: supervision and guidance, critical advice and manuscript proofread, and valuable comments.

Carlos Andrés Betancourt: critical advice and manuscript proofread and valuable comments.

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Correspondence to Jorge Andrés Ramos-Grez.

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Barrionuevo, G.O., Ramos-Grez, J.A., Walczak, M. et al. Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting. Int J Adv Manuf Technol 113, 419–433 (2021). https://doi.org/10.1007/s00170-021-06596-4

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