Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network

Reduction of material usage in 3D printable structures using topology optimization accelerated with U-Net convolutional neural network

Today’s 3D printers allow the creation of very advanced structures from various materials, starting from simple plastics up to metal alloys. Since the printing time and amount of material used to create structures are considered very important in terms of cost and energy consumption, it is better to...

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Bibliographic Details
Main Authors: Rasulzade, J., Maksum, Y., Nogaibayeva, M., Rustamov, S., Akhmetov, Bakytzhan
Other Authors: School of Mechanical and Aerospace Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering
Material reduction
3D printing
Online Access:https://hdl.handle.net/10356/173663

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