Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts
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 wid...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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ETASR
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40734/1/Artificial%20Neural%20Network%20Performance%20Modeling%20and%20Evaluation%20of%20Additive%20Manufacturing%203D%20Printed%20Parts.pdf |
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| author | Subramonian, Sivarao Kadirgama, Kumaran Mahdi Al-Obaidi, Abdulkareem Sh. Mohd Shukor, Mohd Salleh Vatesh, Umesh Kumar Pujari, Satish Rao, Dharsyanth Ramasamy, Devarajan |
| author_facet | Subramonian, Sivarao Kadirgama, Kumaran Mahdi Al-Obaidi, Abdulkareem Sh. Mohd Shukor, Mohd Salleh Vatesh, Umesh Kumar Pujari, Satish Rao, Dharsyanth Ramasamy, Devarajan |
| author_sort | Subramonian, Sivarao |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-04-09T03:52:14Z |
| format | Article |
| id | UMPir40734 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-04-09T03:52:14Z |
| publishDate | 2023 |
| publisher | ETASR |
| record_format | dspace |
| spelling | UMPir407342024-03-22T03:48:16Z http://umpir.ump.edu.my/id/eprint/40734/ Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts Subramonian, Sivarao Kadirgama, Kumaran Mahdi Al-Obaidi, Abdulkareem Sh. Mohd Shukor, Mohd Salleh Vatesh, Umesh Kumar Pujari, Satish Rao, Dharsyanth Ramasamy, Devarajan TJ Mechanical engineering and machinery 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. ETASR 2023 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40734/1/Artificial%20Neural%20Network%20Performance%20Modeling%20and%20Evaluation%20of%20Additive%20Manufacturing%203D%20Printed%20Parts.pdf Subramonian, Sivarao and Kadirgama, Kumaran and Mahdi Al-Obaidi, Abdulkareem Sh. and Mohd Shukor, Mohd Salleh and Vatesh, Umesh Kumar and Pujari, Satish and Rao, Dharsyanth and Ramasamy, Devarajan (2023) Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts. Engineering, Technology & Applied Science Research, 13 (5). pp. 11677-11684. ISSN 1792-8036. (Published) https://doi.org/10.48084/etasr.6185 10.48084/etasr.6185 |
| spellingShingle | TJ Mechanical engineering and machinery Subramonian, Sivarao Kadirgama, Kumaran Mahdi Al-Obaidi, Abdulkareem Sh. Mohd Shukor, Mohd Salleh Vatesh, Umesh Kumar Pujari, Satish Rao, Dharsyanth Ramasamy, Devarajan Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts |
| title | Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts |
| title_full | Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts |
| title_fullStr | Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts |
| title_full_unstemmed | Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts |
| title_short | Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts |
| title_sort | artificial neural network performance modeling and evaluation of additive manufacturing 3d printed parts |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/40734/1/Artificial%20Neural%20Network%20Performance%20Modeling%20and%20Evaluation%20of%20Additive%20Manufacturing%203D%20Printed%20Parts.pdf |
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