Predictive Modeling and Optimization of Tensile and Flexural Strength in FDM 3D Printing Using Decision Trees and Bayesian Optimization.

Open Access

Year : 2024 | Volume :11 | Special Issue : 12 | Page : 203-214
By

Sourabh Anand

Manoj Kumar Satyarthi*

  1. Research Scholar University School of Information, Communication and Technology Delhi India
  2. Assistant Professor University School of Information, Communication and Technology Delhi India

Abstract

This 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.

Keywords: Additive manufacturing, Decision Trees, Bayesian Optimization, Predictive modeling, Mechanical performance

[This article belongs to Special Issue under section in Journal of Polymer and Composites(jopc)]

How to cite this article: Sourabh Anand, Manoj Kumar Satyarthi*. Predictive Modeling and Optimization of Tensile and Flexural Strength in FDM 3D Printing Using Decision Trees and Bayesian Optimization.. Journal of Polymer and Composites. 2024; 11(12):203-214.
How to cite this URL: Sourabh Anand, Manoj Kumar Satyarthi*. Predictive Modeling and Optimization of Tensile and Flexural Strength in FDM 3D Printing Using Decision Trees and Bayesian Optimization.. Journal of Polymer and Composites. 2024; 11(12):203-214. Available from: https://journals.stmjournals.com/jopc/article=2024/view=137640

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Special Issue Open Access Original Research
Volume 11
Special Issue 12
Received January 15, 2024
Accepted February 20, 2024
Published April 1, 2024
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