Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Li Shengpu; Sun Yize

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

机译：Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

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Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R-2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 x 10 (-3), and the mean absolute percentage error (MAPE) is 1.6502 x 10 (-3) for the ink transfer rate of 3D additive printing.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第34期|8642430.1-8642430.12|共12页
作者
Li Shengpu; Sun Yize;
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Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China|Pingdingshan Univ, Coll Informat Engn, Pingdingshan 467002, Peoples R China;

Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China;

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

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