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

Joint Authors

Li, Shengpu
Sun, Yize

Source

Mathematical Problems in Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-09-25

Country of Publication

Egypt

No. of Pages

12

Main Subjects

Civil Engineering

Abstract EN

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 (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

American Psychological Association (APA)

Li, Shengpu& Sun, Yize. 2020. Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1201493

Modern Language Association (MLA)

Li, Shengpu& Sun, Yize. Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1201493

American Medical Association (AMA)

Li, Shengpu& Sun, Yize. Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1201493

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1201493