International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN. 

A Study of 3D Printing Technology and M.L Algorithms for Enhanced Production

    ,

Author Affiliations

  • 1Department of Mechanical Engineering, Dharmsinh Desai University (DDU), Nadiad, India
  • 2Department of Mechanical Engineering, Dharmsinh Desai University (DDU), Nadiad, India

Res. J. Engineering Sci., Volume 14, Issue (1), Pages 34-40, January,26 (2025)

Abstract

The realm of 3D printing technology, also referred to as additive manufacturing, has garnered increasing interest in recent times due to its capacity to construct intricate geometric structures. Fused deposition modeling (FDM) stands out among the various techniques and has gained widespread adoption. However, achieving optimal outcomes with FDM poses a challenge, necessitating meticulous selection of process parameters. Presently, many methodologies rely on Machine Learning (ML) algorithms akin to open-loop systems, offering predictions on printed part properties but lacking quality assurance mechanisms. Conversely, certain closed-loop approaches focus on monitoring a single adjustable processing parameter to assess printed part properties. This study aims to investigate the influence of process parameters and control techniques on mechanical strength, tribology, and other output parameters of production. By providing a comprehensive overview of these developed methods, it aims to facilitate comparison regarding their characteristics, merits, and drawbacks, aiding in the selection of the most suitable approach for specific applications.

References

  1. Feng, Q., Maier, W., & Möhring, H. C. (2022)., Application of machine learning to optimize process parameters in fused deposition modeling of PEEK material., Procedia CIRP, 107, 1-8.
  2. Zhen, H., Zhao, B., Quan, L., & Fu, J. (2023)., Effect of 3D printing process parameters and heat treatment conditions on the mechanical properties and microstructure of PEEK parts., Polymers, 15(9), 2209.
  3. Geng, P., Zhao, J., Gao, Z., Wu, W., Ye, W., Li, G., & Qu, H. (2021)., Effects of printing parameters on the mechanical properties of High-Performance Polyphenylene Sulfide Three-Dimensional Printing., 3D Printing and Additive Manufacturing, 8(1), 33–41. https://doi.org/ 10.1089/3dp.2020.0052
  4. Motaparti, K. P., Taylor, G., Leu, M. C., Chandrashekhara, K., Castle, J., & Matlack, M. (2016)., Effects of build parameters on compression properties for ULTEM 9085 parts by fused deposition modeling.,
  5. Bakhtiari, H.; Aamir, M.; Tolouei-Rad, M. (2023)., Effect of 3D Printing Parameters on the Fatigue Properties of Parts Manufactured by Fused Filament Fabrication: A Review., Appl. Sci., 13, 904. https://doi.org/10.3390/app13020904
  6. Tang, S. M., Cheang, P., AbuBakar, M. S., Khor, K. A., & Liao, K. (2004)., Tension–tension fatigue behavior of hydroxyapatite reinforced polyetheretherketone composites., International Journal of fatigue, 26(1), 49-57.
  7. Mura, A., Ricci, A., & Canavese, G. (2018)., Investigation of fatigue behavior of ABS and PC-ABS polymers at different temperatures., Materials, 11(10), 1818.
  8. Pruitt, L. A. (2000)., Fatigue testing and behavior of plastics., In Mechanical Testing and Evaluation (pp. 758-767). ASM International.
  9. Qayyum, H., Hussain, G., Sulaiman, M., Hassan, M., Ali, A., Muhammad, R., ... & Altaf, K. (2022)., Effect of Raster Angle and Infill Pattern on the In-Plane and Edgewise Flexural Properties of Fused Filament Fabricated Acrylonitrile-Butadiene-Styrene., Applied Sciences, 12(24), 12690.
  10. Gonabadi, H., Chen, Y., Yadav, A., & Bull, S. (2022)., Investigation of the effect of raster angle, build orientation, and infill density on the elastic response of 3D printed parts using finite element microstructural modeling and homogenization techniques., The international journal of advanced manufacturing technology, 1-26.
  11. H., Wang, T., Sun, J., & Yu, Z. (2018)., The effect of process parameters in fused deposition modelling on bonding degree and mechanical properties., Rapid Prototyping Journal, 24(1), 80-92.
  12. Seppala, J. E., Han, S. H., Hillgartner, K. E., Davis, C. S., & Migler, K. B. (2017)., Weld formation during material extrusion additive manufacturing., Soft matter, 13(38), 6761-6769.
  13. Ayrilmis, N. (2018)., Effect of layer thickness on surface properties of 3D printed materials produced from wood flour/PLA filament., Polymer testing, 71, 163-166. https://doi.org/10.1016/j.polymertesting.2018.09.009.
  14. Sood, A. K., R. K. Ohdar and S. S. Mahapatra (2012)., Experimental investigation and empirical modelling of FDM process for compressive strength improvement., Journal of Advanced Research, 3(1), 81-90.
  15. Sood, A. K., A. Equbal, V. Toppo, R. K. Ohdar and S. S. Mahapatra (2012)., An investigation on sliding wear of FDM built parts., CIRP Journal of Manufacturing Science and Technology, 5(1), 48-54
  16. Vijayaraghavan, V., A. Garg, J. S. L. Lam, B. Panda and S. S. Mahapatra (2014)., Process characterisation of 3D-printed FDM components using improved evolutionary computational approach., The International Journal of Advanced Manufacturing Technology, 78(5-8), 781-793.
  17. Vosniakos, G.-C., T. Maroulis and D. Pantelis (2007)., A method for optimizing process parameters in layer-based rapid prototyping., Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(8), 1329-1340.
  18. Equbal, A., A. K. Sood and S. S. Mahapatra (2011)., Prediction of dimensional accuracy in fused deposition modelling- a fuzzy logic approach., International Journal of Productivity and Quality Management, 7(1), 22-43.
  19. Sood, A. K., Ohdar, R. K., & Mahapatra, S. S. (2010)., Parametric appraisal of fused deposition modelling process using the grey Taguchi method., Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224(1), 135-145.
  20. Li, Z., Z. Zhang, J. Shi and D. Wu (2019)., Prediction of surface roughness in extrusion-based additive manufacturing with machine learning., Robotics and Computer-Integrated Manufacturing, 57, 488- 495.
  21. Noriega, A., D. Blanco, B. J. Alvarez and A. Garcia (2013)., Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm., The International Journal of Advanced Manufacturing Technology, 69(9-12), 2301-2313.
  22. Jiang, Z., Liu, Y., Chen, H., & Hu, Q. (2014)., Optimization of process parameters for biological 3D printing forming based on BP neural network and genetic algorithm., In Moving Integrated Product Development to Service Clouds in the Global Economy, (pp. 351-358). IOS Press.
  23. Mohamed, O. A., S. H. Masood and J. L. Bhowmik (2016). "Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing." Advances in Production Engineering & Management 11(3): 227-238., undefined, undefined
  24. Bayraktar, Ö., G. Uzun, R. Çakiroğlu and A. Guldas (2017)., Experimental study on the 3D-printed plastic parts and predicting the mechanical properties using artificial neural networks., Polymers for Advanced Technologies, 28(8), 1044-1051.
  25. Vahabli, E. and S. Rahmati (2016)., Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality., International Journal of Precision Engineering and Manufacturing, 17(12), 1589-1603.
  26. Papazetis, G. and Vosniakos, G.C. (2019)., Mapping of deposition-stable and defect-free additive manufacturing via material extrusion from minimal experiments., Int J Adv Manuf Technol, 100, 2207–2219. https://doi.org/10.1007/s00170-018-2820-1.