Abstract
Due to its distinct production paradigm, additive manufacturing (AM) is positioned to bring about a revolution. It presents the possibility of on-demand, decentralized, and mass-customizable manufacturing. However, several issues related to design principles, standardization, and quality control arise from not only the complexity of production systems but also the need for increasingly complicated and high-quality goods. Artificial Intelligence (AI)-based algorithms, which can effectively monitor quality, optimize processes, model complex systems, and manage energy, is essential in addressing the difficulties. In the present work, we have used three supervised machine learning regression-based algorithms, i.e., XG Boost, Random Forest, and Decision Trees, to determine the Flexural Strength of the Fused Deposition Modeling specimen. The results showed that the XG Boost algorithm resulted in the highest coefficient of determination value of 0.77. Supervised machine learning classification-based algorithms such as the Stochastic Gradient Descent (SGD) algorithm, Decision Tree, and Random Forest algorithm is used to determine good and bad flexural strength specimens. The result showed that the SGD algorithm achieved the highest F1 score of 0.85.
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VSJ, EMS, AVJ, AM, RDD: conceptualization, methodology, writing an original draft, making simulation, review and editing the whole paper. All authors have read and agreed to the published version of the manuscript.
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Jatti, V.S., Jatti, A.V., Mishra, A. et al. Optimizing flexural strength of fused deposition modelling using supervised machine learning algorithms. Int. j. inf. tecnol. 15, 2759–2766 (2023). https://doi.org/10.1007/s41870-023-01329-0
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DOI: https://doi.org/10.1007/s41870-023-01329-0