Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network
Additive Manufacturing (AM) is a pivotal technology for transforming complex geometries with minimal tooling requirements. Among the several AM techniques, Wire Arc Additive Manufacturing (WAAM) is notable for its ability to produce large metal components, which makes it particularly appealing in th...
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MDPI AG
2024-09-01
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| author | Min Seop So Mohammad Mahruf Mahdi Duck Bong Kim Jong-Ho Shin |
| author_facet | Min Seop So Mohammad Mahruf Mahdi Duck Bong Kim Jong-Ho Shin |
| author_sort | Min Seop So |
| collection | DOAJ |
| description | Additive Manufacturing (AM) is a pivotal technology for transforming complex geometries with minimal tooling requirements. Among the several AM techniques, Wire Arc Additive Manufacturing (WAAM) is notable for its ability to produce large metal components, which makes it particularly appealing in the aerospace sector. However, precise control of the bead geometry, specifically bead width and height, is essential for maintaining the structural integrity of WAAM-manufactured parts. This paper introduces a methodology using a Deep Neural Network (DNN) model for forecasting the bead geometry in the WAAM process, focusing on gas metal arc welding cold metal transfer (GMAW-CMT) WAAM. This study addresses the challenges of bead geometry prediction by developing a robust predictive framework. Key process parameters, such as the wire travel speed, wire feed rate, and bead dimensions of the previous layer, were monitored using a Coordinate Measuring Machine (CMM) to ensure precision. The collected data were used to train and validate various regression models, including linear regression, ridge regression, regression, polynomial regression (Quadratic and Cubic), Random Forest, and a custom-designed DNN. Among these, the Random Forest and DNN models were particularly effective, with the DNN showing significant accuracy owing to its ability to learn complex nonlinear relationships inherent in the WAAM process. The DNN model architecture consists of multiple hidden layers with varying neuron counts, trained using backpropagation, and optimized using the Adam optimizer. The model achieved mean absolute percentage error (MAPE) values of 0.014% for the width and 0.012% for the height, and root mean squared error (RMSE) values of 0.122 for the width and 0.153 for the height. These results highlight the superior capability of the DNN model in predicting bead geometry compared to other regression models, including the Random Forest and traditional regression techniques. These findings emphasize the potential of deep learning techniques to enhance the accuracy and efficiency of WAAM processes. |
| format | Article |
| id | doaj-art-6964e5f3c0e84e9a9059fc2ae46bbe8d |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-6964e5f3c0e84e9a9059fc2ae46bbe8d2025-08-20T01:47:34ZengMDPI AGSensors1424-82202024-09-012419625010.3390/s24196250Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural NetworkMin Seop So0Mohammad Mahruf Mahdi1Duck Bong Kim2Jong-Ho Shin3Department of Industrial Engineering, Chosun University, Gwangju 61452, Republic of KoreaDepartment of Electrical and Computer Engineering, Tennessee Tech University, Cookeville, TN 38505, USADepartment of Manufacturing and Engineering Technology, Tennessee Tech University, Cookeville, TN 38505, USADepartment of Industrial Engineering, Chosun University, Gwangju 61452, Republic of KoreaAdditive Manufacturing (AM) is a pivotal technology for transforming complex geometries with minimal tooling requirements. Among the several AM techniques, Wire Arc Additive Manufacturing (WAAM) is notable for its ability to produce large metal components, which makes it particularly appealing in the aerospace sector. However, precise control of the bead geometry, specifically bead width and height, is essential for maintaining the structural integrity of WAAM-manufactured parts. This paper introduces a methodology using a Deep Neural Network (DNN) model for forecasting the bead geometry in the WAAM process, focusing on gas metal arc welding cold metal transfer (GMAW-CMT) WAAM. This study addresses the challenges of bead geometry prediction by developing a robust predictive framework. Key process parameters, such as the wire travel speed, wire feed rate, and bead dimensions of the previous layer, were monitored using a Coordinate Measuring Machine (CMM) to ensure precision. The collected data were used to train and validate various regression models, including linear regression, ridge regression, regression, polynomial regression (Quadratic and Cubic), Random Forest, and a custom-designed DNN. Among these, the Random Forest and DNN models were particularly effective, with the DNN showing significant accuracy owing to its ability to learn complex nonlinear relationships inherent in the WAAM process. The DNN model architecture consists of multiple hidden layers with varying neuron counts, trained using backpropagation, and optimized using the Adam optimizer. The model achieved mean absolute percentage error (MAPE) values of 0.014% for the width and 0.012% for the height, and root mean squared error (RMSE) values of 0.122 for the width and 0.153 for the height. These results highlight the superior capability of the DNN model in predicting bead geometry compared to other regression models, including the Random Forest and traditional regression techniques. These findings emphasize the potential of deep learning techniques to enhance the accuracy and efficiency of WAAM processes.https://www.mdpi.com/1424-8220/24/19/6250wire arc additive manufacturing (WAAM)bead geometrydeep neural network (DNN)gas metal arc welding (GMAW) |
| spellingShingle | Min Seop So Mohammad Mahruf Mahdi Duck Bong Kim Jong-Ho Shin Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network Sensors wire arc additive manufacturing (WAAM) bead geometry deep neural network (DNN) gas metal arc welding (GMAW) |
| title | Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network |
| title_full | Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network |
| title_fullStr | Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network |
| title_full_unstemmed | Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network |
| title_short | Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network |
| title_sort | prediction of metal additively manufactured bead geometry using deep neural network |
| topic | wire arc additive manufacturing (WAAM) bead geometry deep neural network (DNN) gas metal arc welding (GMAW) |
| url | https://www.mdpi.com/1424-8220/24/19/6250 |
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