Prediction of abrasive wear and surface hardness of printed parts by SLA technology
Main Article Content
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.
Keywords
Impresión 3D, estereolitografía SLA, índice de desgaste Taber, dureza superficial, red neuronal artificial, resinas fotocurables 3D printing, SLA Stereolithography, Taber wear index, surface hardness, artificial neural network, light-curing resins
References
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[2] A. Pérez, E. Romero, and A. González, “Three-dimensional printing in helthcare,” Rev. Mex. Ing. Biomédica, vol. 42, no. 2, pp. 32–48, 2021, doi: dx.doi.org/10.17488/RMIB.42.2.3.
[3] T. Wohlers, “Rapid Prototyping & Tooling State of the Industry Annual Worldwide Progress Report Wohlers Report,” 2001. [Online]. Available: www.katzdesign.com,.
[4] E. J. Hurst, “3D Printing in Healthcare: Emerging Applications,” J. Hosp. Librariansh., vol. 16, no. 3, pp. 255–267, Jul. 2016, doi: 10.1080/15323269.2016.1188042.
[5] S. Mishra, “Application of 3D printing in medicine,” Indian Heart J., vol. 68, no. 1, pp. 108–109, Jan. 2016, doi: 10.1016/J.IHJ.2016.01.009.
[6] J. W. Stansbury and M. J. Idacavage, “3D printing with polymers: Challenges among expanding options and opportunities,” Dent. Mater., vol. 32, no. 1, pp. 54–64, Jan. 2016, doi: 10.1016/J.DENTAL.2015.09.018.
[7] A. C. Uzcategui, A. Muralidharan, V. L. Ferguson, S. J. Bryant, and R. R. McLeod, “Understanding and Improving Mechanical Properties in 3D printed Parts Using a Dual-Cure Acrylate-Based Resin for Stereolithography,” Adv. Eng. Mater., vol. 20, no. 12, pp. 1–10, 2018, doi: 10.1002/adem.201800876.
[8] C. Mendes-Felipe, D. Patrocinio, J. M. Laza, L. Ruiz-Rubio, and J. L. Vilas-Vilela, “Evaluation of postcuring process on the thermal and mechanical properties of the Clear02TM resin used in stereolithography,” Polym. Test., vol. 72, pp. 115–121, 2018, doi: 10.1016/j.polymertesting.2018.10.018.
[9] A. Bardelcik, S. Yang, F. Alderson, and A. Gadsden, “The effect of wash treatment on the mechanical properties and energy absorption potential of a 3D printed polymethyl methacrylate (PMMA),” Mater. Today Commun., vol. 26, no. May, p. 101728, 2021, doi: 10.1016/j.mtcomm.2020.101728.
[10] Z. Jiang, Z. Zhang, and K. Friedrich, “Prediction on wear properties of polymer composites with artificial neural networks,” Compos. Sci. Technol., vol. 67, no. 2, pp. 168–176, 2007, doi: 10.1016/j.compscitech.2006.07.026.
[11] A. Tura, H. Lemu, H. Mamo, and A. Santhosh, “Prediction of tensile strength in fused deposition modeling process using artificial neural network and fuzzy logic,” Prog. Addit. Manuf., 2022, doi: https://doi.org/10.1007/s40964-022-00346-y.
[12] M. Tayyab, S. Ahmad, M. J. Akhtar, P. M. Sathikh, and R. M. Singari, “Prediction of mechanical properties for acrylonitrile-butadiene-styrene parts manufactured by fused deposition modelling using artificial neural network and genetic algorithm,” Int. J. Comput. Integr. Manuf., vol. 00, no. 00, pp. 1–18, 2022, doi: 10.1080/0951192X.2022.2104462.
[13] U. S. Tewari, S. K. Sharma, and P. Vasudevan, Polymer tribology, vol. C29, no. 1. 1989.
[14] B. Gupta, “3 - Friction and wear mechanism of polymers, their composites and nanocomposites,” Tribol. Polym. Polym. Compos. Polym. Nanocomposites, pp. 51–117, 2023, doi: https://doi.org/10.1016/B978-0-323-90748-4.00012-1.
[15] E. O. Ezugwu, S. J. Arthur, and E. L. Hines, “Tool-wear prediction using artificial neural networks,” J. Mater. Process. Tech., vol. 49, no. 3–4, pp. 255–264, 1995, doi: 10.1016/0924-0136(94)01351-Z.
[16] C. F. Pérez-Salinas, A. del Olmo, and L. N. López de Lacalle, “Estimation of Drag Finishing Abrasive Effect for Cutting Edge Preparation in Broaching Tool,” Materials (Basel)., vol. 15, no. 15, 2022, doi: 10.3390/ma15155135.
[17] G. Radhakrishnan, C. Kesavan, V. Ramesh, and T. Anandan, “Application of artificial neural network (ANN) for predicting the wear behaviour of Al 2219-SiCp composite,” in Applied Mechanics and Materials, Trans Tech., vol. 852, 2016, pp. 397–401.
[18] B. A. Shuvho, M. A. Chowdhury, and U. K. Debnath, “Analysis of Artificial Neural Network for Predicting Erosive Wear of Nylon-12 Polymer,” vol. 8, no. 1, 2019, doi: 10.1520/MPC20180164.
[19] A. M and C. D, “Journal De Physique. IV: JP - Preface,” J. Phys. IV JP, vol. 11, no. 8, p. 1, 2001.
[20] FormLabs, “Tough Tough Resin for Rugged Prototyping,” 2018.
[21] FormLabs, “Standard Materials for High-Resolution Rapid Prototyping,” 2017.
[22] B. Sen-Crowe, M. Sutherland, M. McKenney, and A. Elkbuli, “A Closer Look Into Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic,” J. Surg. Res., vol. 260, pp. 56–63, 2021, doi: 10.1016/j.jss.2020.11.062.
[23] S. V, “4 - Material Requirements for Plastics Used in Medical Devices,” Plast. Med. Devices, pp. 66–112, 2022, doi: https://doi.org/10.1016/B978-0-323-85126-8.00008-4.
[24] P. Jacobs, “Fundamentals of Stereolithography,” 3D Syst. Inc., 1992.
[25] J. H. Lee, R. K. Prud’homme, and I. A. Aksay, “Cure depth in photopolymerization: Experiments and theory,” Mater. Res. Soc., vol. 16, 2001.
[26] A. El-Aziz, D. Saber, and A. Megahed, “Investigation and Prediction of Abrasive Wear Rate of Heat-Treated HCCIs with Different Cr/C Ratios Using Artificial Neural Networks,” Int. J. Met., vol. 15, no. 4, pp. 1149–1163, 2021, doi: https://doi.org/10.1007/s40962-020-00547-7.
[27] A. Sagbas, F. Kahraman, and U. Esme, “Modeling and predicting abrasive wear behaviour of poly oxy methylenes using response surface methodolgy and neural Networks,” Metalurgija, vol. 48, no. 2, pp. 117–120, 2009.
[28] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.
[29] K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder–decoder approaches,” Proc. SSST 2014 - 8th Work. Syntax. Semant. Struct. Stat. Transl., pp. 103–111, 2014, doi: 10.3115/v1/w14-4012.
[30] C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning (still) requires rethinking generalization,” Commun. ACM, vol. 64, no. 3, pp. 107–115, 2021, doi: 10.1145/3446776.
[31] Y. Zeng, K. Jiang, and J. Chen, “Automatic seismic salt interpretation with deep convolutional neural networks,” Proc. 2019 3rd Int. Conf. Inf. Syst. data Min., pp. 16–20, 2019.
[32] M. Hawryluk and B. Mrzyglod, “A system of analysis and prediction of the loss of forging tool material applying artificial neural networks,” J. Min. Metall. Sect. B Metall., vol. 54, no. 3, pp. 323–337, 2018, doi: 10.2298/JMMB180417023H.
[33] S. Sardar, S. Dey, and D. Das, “Modelling of tribological responses of composites using integrated ANN-GA technique,” J. Compos. Mater., vol. 55, no. 7, pp. 873–896, 2021, doi: https://doi.org/10.1177/0021998320960520.
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Jan 23, 2024
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