A systematic literature review on recent trends of machine learning applications in additive manufacturing
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-022-01957-6.pdf
Reference172 articles.
1. Akhil, V., Raghav, G., Arunachalam, N., & Srinivas, D. S. (2020). Image data-based surface texture characterization and prediction using machine learning approaches for additive manufacturing. Journal of Computing and Information Science in Engineering, 20(2), 1–16. https://doi.org/10.1115/1.4045719
2. Alejandrino, J. D., Concepcion II, R. S., 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. https://doi.org/10.18178/ijmerr.9.9.1253-1263
3. Aljarrah, O., Li, J., Huang, W., Heryudono, A., & Bi, J. (2020). ARIMA-GMDH: A low-order integrated approach for predicting and optimizing the additive manufacturing process parameters. International Journal of Advanced Manufacturing Technology, 106(1–2), 701–717. https://doi.org/10.1007/s00170-019-04315-8
4. Amini, M., Chang, S. I., & Rao, P. (2019). A cybermanufacturing and AI framework for laser powder bed fusion (LPBF) additive manufacturing process. Manufacturing Letters, 21, 41–44. https://doi.org/10.1016/j.mfglet.2019.08.007
5. Aminzadeh, M., & Kurfess, K. R. (2018). Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. Journal of Intelligent Manufacturing, 30, 2505–2523. https://doi.org/10.1007/s10845-018-1412-0
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