Comparative Analysis of 3D Printing Support Structure Prediction Using Feature Selection Methods for Classification Algorithms

Authors

  • Sonali Patil Department of Computer Engineering, Vishwakarma University,Pune, India;
  • Yogesh Deshpande Department of Computer Engineering, Vishwakarma University,Pune, India;
  • Dattatraya Parle Nuclear Advanced Manufacturing Research Centre, Sheffield, United Kingdom

Keywords:

3D printing, Machine learning algorithm, Classification model, Support structure, Dataset, Prediction

Abstract

The success of machine learning models heavily relies on the quality and diversity of the datasets used for training and evaluation. This paper discusses the process of dataset generation and building machine learning models. Support structures are essential for accurate 3D printing of complex geometries, preventing sagging and deformation, and ensuring stable, high-quality prints with careful adjustment of print settings. In this work, different Machine Learning techniques are used and evaluated based on their performance of classifying the need for support during the 3D printing process. The ultimate objective is to classify the model in terms of ‘need support Y/N’.In pursuing this objective, different Machine Learning techniques are utilized to classify different CAD models. The different machine-learning classification techniques applied in this work are Logistic Regression, Support Vector Machine, Random Forest, K-Nearest Neighbours, Decision Tree, and Gradient Boosting. The comparative study based on 6 different performance measures suggests that the Random Forest algorithm works with an accuracy of 0.97 well for classifying the need for support into categories based on the values provided for the process parameters. Finally, SHAP& LIME analysis shows the significance of each feature in the prediction of the need for support. This study can be extended for independent variables including curvature/taper in the build direction and dependent variables as type of structure and type of build adhesion which may be a powerful tool to predict the mechanical properties better.

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Published

24.03.2024

How to Cite

Patil, S. ., Deshpande, Y. ., & Parle, D. . (2024). Comparative Analysis of 3D Printing Support Structure Prediction Using Feature Selection Methods for Classification Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 71–81. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5224

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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