Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm

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dc.authoridKUNCAN, Melih/0000-0002-9749-0418
dc.authoridBayraklilar, Mehmet Said/0000-0002-5365-4441
dc.authoridUlkir, Osman/0000-0002-1095-0160
dc.contributor.authorUlkir, Osman
dc.contributor.authorBayraklilar, Mehmet Said
dc.contributor.authorKuncan, Melih
dc.date.accessioned2024-12-24T19:33:32Z
dc.date.available2024-12-24T19:33:32Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractAs 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 (R2) = 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 R2 = 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.
dc.identifier.doi10.3390/app14052046
dc.identifier.issn2076-3417
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85192486355
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app14052046
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8184
dc.identifier.volume14
dc.identifier.wosWOS:001182866700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectadditive manufacturing
dc.subjectmachine learning
dc.subjectFDM
dc.subjectraster angle
dc.subjectprediction
dc.titleRaster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
dc.typeArticle

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