A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

Author:

Zhang YingORCID,Safdar Mutahar,Xie Jiarui,Li Jinghao,Sage Manuel,Zhao Yaoyao FionaORCID

Funder

Natural Sciences and Engineering Research Council of Canada

Mitacs

Faculty of Engineering, McGill University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Reference217 articles.

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3. Aladesanmi, V., Fatoba, O., Akinlabi, E., & Ikumapayi, O. (2021). Regression analysis of hardness property of laser additive manufactured (LAM) Ti and TiB2 metal matrix composite. Materials Today: Proceedings, 44, 1249–1253.

4. Alasadi, S. A., & Bhaya, W. S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences, 12(16), 4102–4107.

5. Alejandrino, J. D., Concepcion, R. S., II., Lauguico, S. C., Tobias, R. R., Venancio, L., Macasaet, D., Bandala, A. A., & Dadios, E. P. (2020). A machine learning approach of lattice infill pattern for increasing material efficiency in additive manufacturing processes. International Journal of Mechanical Engineering and Robotics Research, 9(9), 1253–1263.

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