Derechos de autor 2024 Investigación e Innovación en Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Exploración del impacto de los parámetros de impresión 3D en las propiedades mecánicas de los materiales ABS y PLA mediante análisis de aprendizaje automático
Corresponding Author(s) : Vladimir Cudris Guerrero
Investigación e Innovación en Ingenierías,
Vol. 12 Núm. 1 (2024): Enero-Junio
Resumen
Objetivo: Esta investigación presenta un estudio experimental exhaustivo sobre el efecto de la temperatura, el material y los parámetros del proceso relacionados con la respuesta de resistencia a la tracción en el proceso de fabricación de impresión 3D con material ABS.
Metodología o método: Se eligió un diseño hipercuadrado latino para la distribución de los puntos experimentales. Se consideraron trece parámetros con múltiples niveles: LAYERHEIGHT, WALLTHICKNESS, TOPBOT-TOMTHICKNESS, TOPBOTTOMLINEDIRECTION1, TOPBOTTOMLINEDIRECTION2, INFILLDENSITY, INFILLLINEDIRECTION1, INFILLLINEDIRECTION2, PRINTSPEED, EXTRUSIONTEMP, BEDTEMP, WORKSPACETEMP y POSITION.
Resultados: Se fabrican probetas de tracción de tipo IV y se ensayan con una máquina universal de ensayos. Para estudiar las relaciones subyacentes entre los factores y la respuesta se utiliza un algoritmo de aprendizaje automático con el software de minería de datos Orange. Los resultados experimentales indican que INFILLDENSITY, TOPBOTOMTOMTHICKNESS e INFILLLINEDIRECTION1 tienen una fuerte correlación positiva con las propiedades mecánicas del ABS y el PLA. Mientras tanto, TOPBOT-TOMLINEDIRECTION1, WORKSPACETEMPERATURE y PRINTSPEED tienen una correlación negativa con la resistencia a la tracción. Las posiciones 1 y 3 con deposiciones de líneas paralelas al eje Y producen la respuesta de mayor resistencia a la tracción.
Conclusiones: Los resultados indican que los algoritmos de máquina pueden utilizarse para estudiar varios parámetros a la vez.
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Referencias
S. Vyavahare, S. Teraiya, D. Panghal, and S. Kumar, ‘Fused deposition modelling: a review’, Rapid Prototyping Journal, vol. 26, pp. 176–201, 2020. DOI: 10.1108/rpj-04-2019-0106
G. Gao, F. Xu, J. Xu, G. Tang, and Z. Liu, “A Survey of the Influence of Process Parameters on Mechanical Properties of Fused Deposition Modeling Parts,” Micromachines, vol. 13, no. 4, p. 553, Mar. 2022. DOI: 10.3390/mi13040553.
A. Dey and N. Yodo, “A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics,” Journal of Manufacturing and Materials Processing, vol. 3, no. 3, p. 64, Jul. 2019. DOI: 10.3390/jmmp3030064.
I. A. Rosid and A. E. Tontowi, ‘Parameter optimization of customized fdm 3d printer machine for biocomposite material [sago/pmma] using 2k fractional factorial design’, OPSI, vol. 14, no. 2, pp. 188–196, 2021. DOI: https://doi.org/10.31315/opsi.v14i2.5352
A. H. M. Haidiezul, M. H. M. Hazwan, W. S. Lee, Gunalan, N. F. Najihah, and I. Fadhli, ‘Shrinkage optimisation on the 3D printed part using Full Factorial Design (FFD) optimisation approach’, IOP Conference Series: Materials Science and Engineering, vol. 932, no. 1, p. 012109, Sep. 2020. DOI 10.1088/1757-899X/932/1/012109
E. R. Bialete et al., ‘Characterization of the Tensile Strength of FDM-Printed Parts Made from Polylactic Acid Filament using 33 Full-Factorial Design of Experiment’, in 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2020, pp. 1–6. DOI: 10.1109/HNICEM51456.2020.9400089
P. Rodríguez, P. Zapico, P. E. Robles, A. Soto, and J. Barreiro, ‘Evaluation of mechanical properties of FDM components reinforced with fibre’, IOP Conference Series: Materials Science and Engineering, vol. 1193, no. 1, p. 012069, Oct. 2021. DOI 10.1088/1757-899X/1193/1/012069
S. Pawar and D. Dolas, ‘Effect of process parameters on flexural strength and surface roughness in fused deposition modeling of PC-ABS material’, Journal of Micromanufacturing, vol. 5, no. 2, pp. 164–170, 2022. https://doi.org/10.1177/25165984211031
T. Nancharaiah, ‘Optimization of Process Parameters in FDM Process Using Design of Experiments’, 2011.
R. Srinivasan, T. Pridhar, L. S. Ramprasath, N. S. Charan, and W. Ruban, ‘Prediction of tensile strength in FDM printed ABS parts using response surface methodology (RSM)’, Materials Today: Proceedings, vol. 27, pp. 1827–1832, 2020. https://doi.org/10.1016/j.matpr.2020.03.788
G. Gao, F. Xu, and J. Xu, “Parametric Optimization of FDM Process for Improving Mechanical Strengths Using Taguchi Method and Response Surface Method: A Comparative Investigation,” Machines, vol. 10, no. 9, p. 750, Aug. 2022, doi: 10.3390/machines10090750.
O. A. Mohamed, S. H. Masood, and J. L. Bhowmik, ‘Mathematical modeling and FDM process parameters optimization using response surface methodology based on Q-optimal design’, Applied Mathematical Modelling, vol. 40, no. 23, pp. 10052–10073, 2016. https://doi.org/10.1016/j.apm.2016.06.055
M. Waseem et al., “Multi-Response Optimization of Tensile Creep Behavior of PLA 3D Printed Parts Using Categorical Response Surface Methodology,” Polymers, vol. 12, no. 12, p. 2962, Dec. 2020, doi: 10.3390/polym12122962.
M. Vorkapić, I. Mladenović, M. Pergal, T. Ivanov, and M. Baltić, ‘Optimisation of tensile stress of poly (lactic acid) 3D printed materials using response surface methodology’, Tribology and Materials, vol. 1, no. 2, pp. 70–80, 2022. https://doi.org/10.46793/tribomat.2022.009
M. Hikmat, S. Rostam, and Y. M. Ahmed, ‘Investigation of tensile property-based Taguchi method of PLA parts fabricated by FDM 3D printing technology’, Results in Engineering, vol. 11, p. 100264, 2021. https://doi.org/10.1016/j.rineng.2021.100264
M. Kam, A. İpekçi, and Ö. Şengül, ‘Investigation of the effect of FDM process parameters on mechanical properties of 3D printed PA12 samples using Taguchi method’, Journal of Thermoplastic Composite Materials, vol. 36, no. 1, pp. 307–325, 2023. https://doi.org/10.1177/089270572110064
N. Maguluri, G. Suresh, and K. V. Rao, ‘Assessing the effect of FDM processing parameters on mechanical properties of PLA parts using Taguchi method’, Journal of Thermoplastic Composite Materials, vol. 36, no. 4, pp. 1472–1488, 2023. https://doi.org/10.1177/08927057211053
K. Sharma, K. Kumar, K. R. Singh, and M. S. Rawat, ‘Optimization of FDM 3D printing process parameters using Taguchi technique’, IOP Conference Series: Materials Science and Engineering, vol. 1168, no. 1, p.012022, Jul. 2021. DOI 10.1088/1757-899X/1168/1/012022
M. Abouelmajd et al., ‘Experimental analysis and optimization of mechanical properties of FDM-processed polylactic acid using Taguchi design of experiment’, International Journal for Simulation and Multidisciplinary Design Optimization, vol. 12, p. 30, 2021. https://doi.org/10.1051/smdo/2021031
G. D. Goh, S. L. Sing, and W. Y. Yeong, ‘A review on machine learning in 3D printing: applications, potential, and challenges’,Artificial Intelligence Review, vol. 54, no. 1, pp. 63–94, 2021. https://doi.org/10.1007/s10462-020-09876-9
N. S. Johnson et al., ‘Machine Learning for Materials Developments in Metals Additive Manufacturing’, arXiv preprintarXiv:2005. 05235, 2020. https://doi.org/10.48550/arXiv.2005.05235
T. Sarkar, ‘doepy’, GitHub repository. GitHub. Available: https://github.com/tirthajyoti/doepy
ASTM International, ASTM D638-14, Standard Test Method for Tensile Properties of Plastics. ASTM International, 2015. DOI: 10.1520/D0638-14
U. of L. Bioinformatics Laboratory, “Data Mining,” Orange Data Mining - Data Mining, https://orangedatamining.com/ (accessed Oct. 24, 2023).
U. of L. Bioinformatics Laboratory, “oRANGE Correlations,” Orange Data Mining - Data Mining, https://orangedatamining.com/widget-catalog/unsupervised/correlations/ (accessed Oct. 24, 2023).