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Predictive modeling of dimensional accuracies in 3D printing using artificial neural network

Additive manufacturing, particularly Fused Deposition Modeling (FDM) using three-dimensional (3D) printing, has revolutionized the manufacturing industry by offering design flexibility, customization options, affordability, and high printing speed. However, improper selection of process parameters...

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Main Authors: Subramonian, Sivarao, Kumaran, K., Dharsyanth, R., Md Ali, Mohd Amran, Salleh, Mohd Shukor, Pujari, Satish, Ramasamy, Devarajan, Vatesh, Umesh Kumar, Mahdi Al-Obaidi, Abdulkareem Sh, Ramesh, S., Lee, Kit Yee Sara
Format: Article
Language:English
Published: Taylor's University 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27419/2/0061718122023.PDF
http://eprints.utem.edu.my/id/eprint/27419/
https://jestec.taylors.edu.my/Vol%2018%20Issue%204%20August%202023/18_4_24.pdf
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spelling my.utem.eprints.274192024-07-23T09:11:26Z http://eprints.utem.edu.my/id/eprint/27419/ Predictive modeling of dimensional accuracies in 3D printing using artificial neural network Subramonian, Sivarao Kumaran, K. Dharsyanth, R. Md Ali, Mohd Amran Salleh, Mohd Shukor Pujari, Satish Ramasamy, Devarajan Vatesh, Umesh Kumar Mahdi Al-Obaidi, Abdulkareem Sh Ramesh, S. Lee, Kit Yee Sara Additive manufacturing, particularly Fused Deposition Modeling (FDM) using three-dimensional (3D) printing, has revolutionized the manufacturing industry by offering design flexibility, customization options, affordability, and high printing speed. However, improper selection of process parameters in FDM can lead to suboptimal surface efficiency, defective mechanical properties, increased waste, and higher production costs. In this research, an Artificial Neural Network (ANN) model was developed to optimize dimensional properties in FDM by considering control factors such as layer thickness, orientation, raster angle, raster width, and air gap. Experimental data consisting of 27 sets of control parameters and corresponding dimensional outputs were used to train and validate the ANN model. The ANN model was developed using MATLAB software, employing training functions and learning algorithms to optimize the neural network architecture. The optimized ANN structure comprised 15 neurons and 2 layers, and it demonstrated accurate prediction of dimensional properties with percentage errors ranging from 0.01% to 25.49% for length, less than 10% for weight, and less than 4% for thickness. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to quantify the errors, indicating the effectiveness of the ANN model in predicting dimensional properties. The results highlight the potential of ANN in optimizing FDM process parameters for improved dimensional accuracy. The ANN model provides a reliable tool for manufacturers to predict and optimize the length, weight, and thickness of 3D-printed components, leading to enhanced product quality and reduced production costs. The developed ANN model can be further extended to consider other parameters and optimize various aspects of the additive manufacturing process. Taylor's University 2023 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27419/2/0061718122023.PDF Subramonian, Sivarao and Kumaran, K. and Dharsyanth, R. and Md Ali, Mohd Amran and Salleh, Mohd Shukor and Pujari, Satish and Ramasamy, Devarajan and Vatesh, Umesh Kumar and Mahdi Al-Obaidi, Abdulkareem Sh and Ramesh, S. and Lee, Kit Yee Sara (2023) Predictive modeling of dimensional accuracies in 3D printing using artificial neural network. Journal Of Engineering Science And Technology, 18 (4). pp. 2148-2160. ISSN 1823-4690 https://jestec.taylors.edu.my/Vol%2018%20Issue%204%20August%202023/18_4_24.pdf
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Additive manufacturing, particularly Fused Deposition Modeling (FDM) using three-dimensional (3D) printing, has revolutionized the manufacturing industry by offering design flexibility, customization options, affordability, and high printing speed. However, improper selection of process parameters in FDM can lead to suboptimal surface efficiency, defective mechanical properties, increased waste, and higher production costs. In this research, an Artificial Neural Network (ANN) model was developed to optimize dimensional properties in FDM by considering control factors such as layer thickness, orientation, raster angle, raster width, and air gap. Experimental data consisting of 27 sets of control parameters and corresponding dimensional outputs were used to train and validate the ANN model. The ANN model was developed using MATLAB software, employing training functions and learning algorithms to optimize the neural network architecture. The optimized ANN structure comprised 15 neurons and 2 layers, and it demonstrated accurate prediction of dimensional properties with percentage errors ranging from 0.01% to 25.49% for length, less than 10% for weight, and less than 4% for thickness. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to quantify the errors, indicating the effectiveness of the ANN model in predicting dimensional properties. The results highlight the potential of ANN in optimizing FDM process parameters for improved dimensional accuracy. The ANN model provides a reliable tool for manufacturers to predict and optimize the length, weight, and thickness of 3D-printed components, leading to enhanced product quality and reduced production costs. The developed ANN model can be further extended to consider other parameters and optimize various aspects of the additive manufacturing process.
format Article
author Subramonian, Sivarao
Kumaran, K.
Dharsyanth, R.
Md Ali, Mohd Amran
Salleh, Mohd Shukor
Pujari, Satish
Ramasamy, Devarajan
Vatesh, Umesh Kumar
Mahdi Al-Obaidi, Abdulkareem Sh
Ramesh, S.
Lee, Kit Yee Sara
spellingShingle Subramonian, Sivarao
Kumaran, K.
Dharsyanth, R.
Md Ali, Mohd Amran
Salleh, Mohd Shukor
Pujari, Satish
Ramasamy, Devarajan
Vatesh, Umesh Kumar
Mahdi Al-Obaidi, Abdulkareem Sh
Ramesh, S.
Lee, Kit Yee Sara
Predictive modeling of dimensional accuracies in 3D printing using artificial neural network
author_facet Subramonian, Sivarao
Kumaran, K.
Dharsyanth, R.
Md Ali, Mohd Amran
Salleh, Mohd Shukor
Pujari, Satish
Ramasamy, Devarajan
Vatesh, Umesh Kumar
Mahdi Al-Obaidi, Abdulkareem Sh
Ramesh, S.
Lee, Kit Yee Sara
author_sort Subramonian, Sivarao
title Predictive modeling of dimensional accuracies in 3D printing using artificial neural network
title_short Predictive modeling of dimensional accuracies in 3D printing using artificial neural network
title_full Predictive modeling of dimensional accuracies in 3D printing using artificial neural network
title_fullStr Predictive modeling of dimensional accuracies in 3D printing using artificial neural network
title_full_unstemmed Predictive modeling of dimensional accuracies in 3D printing using artificial neural network
title_sort predictive modeling of dimensional accuracies in 3d printing using artificial neural network
publisher Taylor's University
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27419/2/0061718122023.PDF
http://eprints.utem.edu.my/id/eprint/27419/
https://jestec.taylors.edu.my/Vol%2018%20Issue%204%20August%202023/18_4_24.pdf
_version_ 1806429026232827904
score 13.211869

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