Optimization of coating variables for hardness of industrial tools by using artificial neural networks-Reference-Cited by-同舟云学术

Optimization of coating variables for hardness of industrial tools by using artificial neural networks

Published:2011-09 Issue:10 Volume:38 Page:12116-12127
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Yazdi M. Reza Soleymany,Khorasani A. Mahyar,Faraji Mehdi

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference25 articles.

1. Abaspor, H., Shakeri, M., & Abadi, H. A. (2007). Prediction of maximum cutting deep in water jet machining process by using neural networks. In First National Conference of Manufacturing and Design, pp. 177–187.

2. Materials evaluation of PVD/CVD Coated WC/Co super hard alloys by the ultrasonic microscopy;Aizawa;Nondestructive Testing and Evaluation,1992

3. Electrochemical evaluation of Ti–13Nb–13Zr, Ti–6Al–4V and Ti–6Al–7Nb alloys for biomedical application by long-term immersion tests;Assis;Journal of Materials and Corrosion,2007

4. Buitrago, B., Irausquín, I., & Mendoza, J. (2007). Ultrasonic Evaluation of a Beta-C Titanium Alloy. In fourth international Conference on NDT, pp. 11–14.

5. Pattern recognition: A statistical approach;Devijver,1982

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