Warpage detection in 3D printing of polymer parts: a deep learning approach
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02414-2.pdf
Reference43 articles.
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2. Armillotta, A., Bellotti, M., & Cavallaro, M. (2018). Warpage of FDM parts: Experimental tests and analytic model. Robotics and Computer-Integrated Manufacturing, 50, 140–152. https://doi.org/10.1016/J.RCIM.2017.09.007.
3. Bedi, P., Goyal, S. B., Rajawat, A. S., Bhaladhare, P., Aggarwal, A., & Prasad, A. (2023). Feature correlated auto encoder method for industrial 4.0 process inspection using computer vision and machine learning. Procedia Comput Sci, 218, 788–798. https://doi.org/10.1016/J.PROCS.2023.01.059.
4. Bhandarkar, V. V., Patil, I. G., Shahare, H. Y., & Tandon, P. (2023). Understanding the Influence of Process Parameters for Minimizing Defects in 3D Printed Parts Through Remote Monitoring. ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) 2-A. https://doi.org/10.1115/IMECE2022-93991.
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