Prediction of abrasive wear and surface hardness of printed parts by SLA technology

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Published: Jan 23, 2024
Updated: 2024-01-23
Versions:
2024-01-23 (3)
DOI: https://doi.org/10.17163/ings.n31.2024.02
Keywords:
3D printing, SLA Stereolithography, Taber wear index, surface hardness, artificial neural network, light-curing resins

Main Article Content

P. Muñoz-Valverde
https://orcid.org/0000-0001-9043-2461
O. Villena-López
https://orcid.org/0000-0002-4749-1850
L. Mayorga-Ases
https://orcid.org/0000-0003-0586-2390
CristianUnviersidad Técnica de AmbatoC. Pérez-Salinas
https://orcid.org/0000-0003-4031-5464
D. Moya
https://orcid.org/0000-0003-4839-4259

Abstract

In the present study, a prediction of hardness deterioration and abrasive wear was performed through a neural network using artificial intelligence on a material printed in SLA. This article aims to predict the mechanical properties, wear resistance and surface hardness of parts manufactured by SLA stereolithography printing. A full factorial DOE was used to associate the peculiar parameters (print orientation, cure time, layer height) to perform experiments. The mechanical properties were evaluated according to ASTM regulations, with the objective of obtaining feeding data and validation of the predictions of the Taber Wear Index and hardness using an artificial neural network. The experimental results are in good agreement with the measured data with satisfactory prediction errors with a mean square error (MSE) of 0.01 corresponding to abrasive wear using the clear resin and a mean absolute error (MSE) of 0.09 with an R2 of 0.756, the prediction with the neural network with a mean square error (MSE) of 2.47 corresponding to abrasive wear using the tough resin and a mean absolute error (MSE) of 14.3 with an R2 of 0.97. It was shown that the accuracy of the prediction is reasonable, and the network has the potential to be improved if the experimental database for training the network could be expanded. Therefore, wear and hardness mechanical properties can be predicted appropriately with an ANN.

Article Details

Issue
No. 31 (2024): january-june
Section
Additive Manufacturing
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