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Sourabh Anand, Manoj Kumar Satyarthi*
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- Research Scholar, Assistant Professor, University School of Information, Communication and Technology, University School of Information, Communication and Technology, Delhi, Delhi, India, India
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Abstract
nThis research investigates predictive modelling and optimization technique for the tensile and flexural strength of PlA (Poly Lactic Acid) in Fused Deposition Modelling (FDM) 3D printing. Employing Decision Trees and Bayesian Optimization enhances comprehension and control of 3D printing process. Precise model predicts PLA material properties based on input parameters. Methodology involves rigorous data preprocessing, encompassing, cleaning, transformation, and normalization. Hyperparameter optimization via grid search systematically explores configurations, optimizing model performance. Bayesian Optimization further refines the model. Results exhibit significance, with the highest Tensile Mean Square Error at 5.5308 × 10-5 and a Tensile Root Mean Square Error of 0.007437, emphasizing findings’ importance. The R-squared coefficient, at 0.94071, signifies substantial explanatory power. The optimized flexural model yields a notable best Flexural Mean Squared Error of 0.12583. With a respectable Flexural Root Mean Squared Error of 0.35472, the model demonstrates accuracy. A substantial R-squared value of 0.82573 indicates a robust correlation between predictor variables and observed flexural response.
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Keywords: Additive manufacturing, Decision Trees, Bayesian Optimization, Predictive modeling, Mechanical performance
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References
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| Volume | ||
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | ||
| Received | January 15, 2024 | |
| Accepted | February 20, 2024 | |
| Published | April 1, 2024 at 4:49 pm |
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