Prediction of the mechanical properties of TPMS structures based on Back propagation neural network-Reference-Cited by-同舟云学术

Prediction of the mechanical properties of TPMS structures based on Back propagation neural network

Published:2024-01-29 Issue: Volume: Page:1-13
ISSN:1025-5842
Container-title:Computer Methods in Biomechanics and Biomedical Engineering
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering

Author:

Li Jiayao¹²,Luo Ketong¹²,Qi Wen³,Du Jun¹²,Huang Yanqun¹²,Lu Chun¹²^ORCID

Affiliation:

1. School of Materials and Environment, Guangxi Minzu University, Nanning, China

2. A Guangxi Key Laboratory of Advanced Structural Materials and Carbon Neutralization, Guangxi Colleges and Universities Key Laboratory of Environmental-friendly Materials and New Technology for Carbon Neutralization, Guangxi Minzu University, Nanning, China

3. Chemical Department, Guangxi Minzu University, Nanning, China

Funder

Guangxi Science and Technology Major Project

Major Special Project of Guangxi Science and Technology Department

Guangxi Provincial Science and Technology Program

Guangxi Natural Science Foundation

Nanning Qingxiu District Science and Technology Major Special Project

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10255842.2024.2307917

Reference33 articles.

1. Functionally graded and multi-morphology sheet TPMS lattices: Design, manufacturing, and mechanical properties

2. Polycrystalline Diamond as a Potential Material for the Hard-on-Hard Bearing of Total Hip Prosthesis: Von Mises Stress Analysis

3. Flow and mass transfer prediction in anisotropic TPMS-structures as extracorporeal oxygenator membranes using reduced order modeling

4. Mechanical properties and deformation behavior of functionally graded TPMS structures under static and dynamic loading

5. Porous scaffold design by solid T-splines and triply periodic minimal surfaces