Volume: 11 | Issue: 1 | Year 2025 | Subscription
Received Date: 01/30/2025
Acceptance Date: 02/14/2025
Published On: 2025-03-10
First Page: 6
Last Page: 15
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By: Kartik Mandavia and Dhwanik Patel
1 Professor, Department of Mechanical Engineering, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India
2 Professor, Department of Mechanical Engineering, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India
Abstract
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.
Keywords: 3-D printer, FDM, machine learning, control system, finite element analysis
Citation:
How to cite this article: Kartik Mandavia and Dhwanik Patel, A Study of 3D Printing Technology and M.L. Algorithms for Enhanced Production. International Journal of Computer Aided Manufacturing. 2025; 11(1): 6-15p.
How to cite this URL: Kartik Mandavia and Dhwanik Patel, A Study of 3D Printing Technology and M.L. Algorithms for Enhanced Production. International Journal of Computer Aided Manufacturing. 2025; 11(1): 6-15p. Available from:https://journalspub.com/publication/ijcam/article=19308
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