Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
As additive manufacturing (AM) processes become integrated with artificial intelligence systems, the time and cost of the fabrication process decrease. In this study, the raster angle, an important parameter in the manufacturing process, was examined using fused deposition modeling (FDM), an AM meth...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/2046 |
| _version_ | 1797264802314190848 |
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| author | Osman Ulkir Mehmet Said Bayraklılar Melih Kuncan |
| author_facet | Osman Ulkir Mehmet Said Bayraklılar Melih Kuncan |
| author_sort | Osman Ulkir |
| collection | DOAJ |
| description | As additive manufacturing (AM) processes become integrated with artificial intelligence systems, the time and cost of the fabrication process decrease. In this study, the raster angle, an important parameter in the manufacturing process, was examined using fused deposition modeling (FDM), an AM method. The optimal value of this parameter varies depending on the designed product geometry. By changing the raster angle, the distribution of stresses and strains within the printed object can be modified, potentially influencing the mechanical behavior of the object. Thus, the correct estimation of the raster angle is essential for obtaining parts with high mechanical properties. The focus of this study is to reduce the fabrication time and cost of products by intertwining machine learning (ML) systems with mechanical systems. Its novelty is that ML has never been applied for FDM raster angle estimation. The estimation and modeling of the raster angle were performed using five different ML algorithms. These algorithms include a support vector machine (SVM), Gaussian process regression (GPR), an artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). Data for training were generated using various shapes and geometries, then trained in the MATLAB software, and a prediction model between the input parameters and the raster angle was created. The predicted model was evaluated using five performance criteria. The RFR model predicts the raster angle in the FDM test data with R-squared (R<sup>2</sup>) = 0.92, an explained variance score (EVS) = 0.92, a mean absolute error (MAE) = 0.012, a root mean square error (RMSE) = 0.056, and a mean squared error (MSE) = 0.0032. These values are R<sup>2</sup> = 0.93, EVS = 0.93, MAE = 0.010, RMSE = 0.051, and MSE0.0025 for the training data. RFR is significantly superior to the other prediction algorithms. The proposed model predicts the optimum raster angle for any geometry. |
| first_indexed | 2024-04-25T00:34:41Z |
| format | Article |
| id | doaj.art-ba12a27af70d42c2aaa9b2dda80c134d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:34:41Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ba12a27af70d42c2aaa9b2dda80c134d2024-03-12T16:39:55ZengMDPI AGApplied Sciences2076-34172024-02-01145204610.3390/app14052046Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning AlgorithmOsman Ulkir0Mehmet Said Bayraklılar1Melih Kuncan2Department of Electric and Energy, Mus Alparslan University, Mus 49210, TurkeyDepartment of Civil Engineering, Siirt University, Siirt 56100, TurkeyDepartment of Electrical and Electronics Engineering, Siirt University, Siirt 56100, TurkeyAs additive manufacturing (AM) processes become integrated with artificial intelligence systems, the time and cost of the fabrication process decrease. In this study, the raster angle, an important parameter in the manufacturing process, was examined using fused deposition modeling (FDM), an AM method. The optimal value of this parameter varies depending on the designed product geometry. By changing the raster angle, the distribution of stresses and strains within the printed object can be modified, potentially influencing the mechanical behavior of the object. Thus, the correct estimation of the raster angle is essential for obtaining parts with high mechanical properties. The focus of this study is to reduce the fabrication time and cost of products by intertwining machine learning (ML) systems with mechanical systems. Its novelty is that ML has never been applied for FDM raster angle estimation. The estimation and modeling of the raster angle were performed using five different ML algorithms. These algorithms include a support vector machine (SVM), Gaussian process regression (GPR), an artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). Data for training were generated using various shapes and geometries, then trained in the MATLAB software, and a prediction model between the input parameters and the raster angle was created. The predicted model was evaluated using five performance criteria. The RFR model predicts the raster angle in the FDM test data with R-squared (R<sup>2</sup>) = 0.92, an explained variance score (EVS) = 0.92, a mean absolute error (MAE) = 0.012, a root mean square error (RMSE) = 0.056, and a mean squared error (MSE) = 0.0032. These values are R<sup>2</sup> = 0.93, EVS = 0.93, MAE = 0.010, RMSE = 0.051, and MSE0.0025 for the training data. RFR is significantly superior to the other prediction algorithms. The proposed model predicts the optimum raster angle for any geometry.https://www.mdpi.com/2076-3417/14/5/2046additive manufacturingmachine learningFDMraster angleprediction |
| spellingShingle | Osman Ulkir Mehmet Said Bayraklılar Melih Kuncan Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm Applied Sciences additive manufacturing machine learning FDM raster angle prediction |
| title | Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm |
| title_full | Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm |
| title_fullStr | Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm |
| title_full_unstemmed | Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm |
| title_short | Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm |
| title_sort | raster angle prediction of additive manufacturing process using machine learning algorithm |
| topic | additive manufacturing machine learning FDM raster angle prediction |
| url | https://www.mdpi.com/2076-3417/14/5/2046 |
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