Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts
Received: 10 July 2023 | Revised: 28 July 2023 | Accepted: 2 August 2023 | Online: 29 August 2023
Corresponding author: Kumaran Kadirgama
This research article presents a comprehensive study on the performance modeling of 3D printed parts using Artificial Neural Networks (ANNs). The aim of this study is to optimize the mechanical properties of 3D printed components through accurate prediction and analysis. The study focuses on the widely employed Fused Deposition Modeling (FDM) technique. The ANN model is trained and validated using experimental data, incorporating input parameters such as temperature, speed, infill direction, and layer thickness to predict mechanical properties including yield stress, Young's modulus, ultimate tensile strength, flexural strength, and elongation at fracture. The results demonstrate the effectiveness of the ANN model with an average error below 10%. The study also reveals the significant impact of process parameters on the mechanical properties of 3D printed parts and highlights the potential for optimizing these parameters to enhance the performance of printed components. The findings of this research contribute to the field of additive manufacturing by providing valuable insights into the optimization of 3D printing processes and facilitating the development of high-performance 3D printed components.
Keywords:3D printing, Artificial Neural Networks (ANNs), predictive modeling, Fused Deposition Modeling (FDM), mechanical properties
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Copyright (c) 2023 Sivarao Subramonian, Kumaran Kadirgama, Mohd Shukor Mohd Salleh, Devarajan Ramasamy, Dharsyanth Rao, Sara Lee Kit Yee, Satish Pujari, Zuhair Khalim
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