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ORIGINAL RESEARCH article

Front. Mater.
Sec. Polymeric and Composite Materials
Volume 11 - 2024 | doi: 10.3389/fmats.2024.1410277

Prediction of Machine Learning-Based Hardness for the Polycarbonate using Additive Manufacturing

Provisionally accepted
Sachin Salunkhe Sachin Salunkhe 1*Haitham A. Mahmoud Haitham A. Mahmoud 2Shanmugasundar G Shanmugasundar G 3Swapnil Vyavahare Swapnil Vyavahare 4Rakesh Kumar Rakesh Kumar 5Robert Cep Robert Cep 6Sharad Gawade Sharad Gawade 7Emad Abouel Nasr Emad Abouel Nasr 2
  • 1 Gazi University, Ankara, Türkiye
  • 2 King Saud University, Riyadh, Riyadh, Saudi Arabia
  • 3 Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India
  • 4 Dr. Vithalrao Vikhe Patil College of Engineering, Ahmednagar, India
  • 5 Jain University, Bengaluru, Karnataka, India
  • 6 Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, Moravian-Silesian Region, Czechia
  • 7 Sharadchandra Pawar College of Engineering and Technology, Baramati, Maharashtra, India

The final, formatted version of the article will be published soon.

    Additive manufacturing (AM) is a revolutionary technology transforming traditional production processes by providing exceptional mechanical characteristics. The present study aims explicitly to predict the hardness of Polycarbonate (PC) parts produced using AM. The objectives of this study are: (1) To investigate the process parameters that impact the ability to estimate the hardness of PC materials accurately, and (2) To develop a best-performing ML model from a range of models that can reliably predict the hardness of additively manufactured PC parts. Initially, fused filament fabrication (FFF), the most affordable AM technique, was used for the manufacturing of parts. Four process parameters, infill density, print direction, raster angle, and layer thickness, are selected for investigation. A heatmap is generated to obtain the influence of process parameters on hardness. Then, machine learning (ML) techniques create a range of predictive models that can predict hardness value considering the level of process parameters. The developed ML models include Linear Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Regression, AdaBoost, and Artificial Neural Network. Further, an investigation has been done that includes choosing and improving ML algorithms and assessing the models' performance.Prediction plots, residual plots, and evaluation metrics plots are prepared to gauge the performance of the developed models. Thus, the research enhances AM capabilities by applying predictive modeling to process parameters and improving the quality and reliability of fabricated components.

    Keywords: Additive manufacturing, Hardness, Polycarbonate, Fused filament fabrication, machine learning

    Received: 31 Mar 2024; Accepted: 15 Aug 2024.

    Copyright: © 2024 Salunkhe, A. Mahmoud, G, Vyavahare, Kumar, Cep, Gawade and Abouel Nasr. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sachin Salunkhe, Gazi University, Ankara, Türkiye

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.