BP neural network based flexural strength prediction of open-porous Cu-Sn-Ti composites-Reference-Cited by-同舟云学术

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BP neural network based flexural strength prediction of open-porous Cu-Sn-Ti composites

Published:2018-06 Issue:3 Volume:28 Page:315-324
ISSN:1002-0071
Container-title:Progress in Natural Science: Materials International
language:en
Short-container-title:Progress in Natural Science: Materials International

Author:

Zhao Biao,Yu Tianyu,Ding Wenfeng,Li Xianying,Su Honghua

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

General Materials Science

Reference37 articles.

1. Microstructure and Performance of Porous Ni-Cr Alloy Bonded Diamond Grinding Wheel

2. Simulation study on effect of cutting parameters and cooling mode on bone-drilling temperature field of superhard drill

3. Effects of Nb content in Ti–Ni–Nb brazing alloys on the microstructure and mechanical properties of Ti–22Al–25Nb alloy brazed joints

4. Experimental and modeling characterization of wear and life expectancy of electroplated CBN grinding wheels

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1. Effect of TiH2 addition amounts on the microstructures and properties of porous Cu-Sn-Ti diamond tools;Tribology International;2025-01

2. Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery;Coordination Chemistry Reviews;2024-09

3. Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach;Modelling;2024-07-30

4. Effects of three different powder pre-processing and two-step sintering processes on the microstructure and mechanical properties of Cu–Sn–Ti porous matrix materials;Journal of Materials Research and Technology;2024-05

5. Research on Intelligent Courses in English Education based on Neural Networks;Annals of Data Science;2024-04-25

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