Predicción de desgaste abrasivo y dureza superficial de partes impresas por tecnología SLA

• Muñoz-Valverde, P. ^[1] ; Villena-López, O. ^[2] ; Mayorga-Ases, L. ^[3] ; Pérez-Salinas, CristianUnviersidad Técnica de AmbatoC. ^[2] ; Moya, D. ^[2]
  1. [1] Escuela Politécnica del Ejército

     Escuela Politécnica del Ejército

     Sangolqui, Ecuador

     
  2. [2] Universidad Técnica de Ambato

     Universidad Técnica de Ambato

     Ambato, Ecuador

     
  3. [3] Unviersidad Técnica de Ambato

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• Localización: Ingenius: Revista de Ciencia y Tecnología, ISSN 1390-650X, ISSN-e 1390-860X, Nº. 31, 2024 (Ejemplar dedicado a: january-june), págs. 19-31
• Idioma: español
• DOI: 10.17163/ings.n31.2024.02
• Títulos paralelos:
  • Prediction of abrasive wear and surface hardness of printed parts by SLA technology
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• Resumen
  • español

    En el presente estudio se realizó una predicción del deterioro de la dureza y el desgaste abrasivo a través de una red neuronal utilizando inteligencia artificial sobre un material impreso en SLA. Esta investigación tiene como objetivo predecir las propiedades mecánicas de resistencia al desgaste y dureza superficial de piezas fabricadas mediante impresión por estereolitografía (SLA). Para realizar los experimentos se utilizó un diseño factorial de dos niveles o DOE factorial completo y así asociar los parámetros peculiares (orientación de impresión, tiempo de curado, altura de la capa). Las propiedades mecánicas fueron evaluadas según normativas ASTM, con el objetivo de obtener datos de alimentación y validación de las predicciones del índice de desgaste Taber y la dureza empleando una red neuronal artificial. Los resultados experimentales concuerdan con los datos medidos con errores de predicción satisfactorios con un error cuadrático medio (MSE) de 0,01 correspondiente al desgaste abrasivo utilizando la resina transparente y un error absoluto medio (MSE) de 0,09 con un R2 de 0,76. La predicción con la red neuronal tiene un error cuadrático medio (MSE) de 2.47 perteneciente al desgaste abrasivo utilizando la resina resistente y un error absoluto medio (MSE) de 14,3 con un R2 de 0,97. Se demostró que la precisión de la predicción es razonable, y que la red tiene potencial para mejorar si se pudiera ampliar la base de datos experimental para entrenar la red. Por lo tanto, las propiedades mecánicas de desgaste y dureza se pueden predecir, adecuadamente, con una RNA.

  • English

    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.

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