Using artificial intelligence to predict surface roughness in deep drilling of steel components-Reference-Cited by-同舟云学术

Using artificial intelligence to predict surface roughness in deep drilling of steel components

Published:2011-01-30 Issue:5 Volume:23 Page:1893-1902
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Bustillo Andres,Correa Maritza

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/content/pdf/10.1007/s10845-011-0506-8.pdf

Reference36 articles.

1. 1302 I. (1992). Technical drawings—method of indicating surface texture. International organization for standardization.

2. 4288 I. (1996). Geometrical product specifications (GPS)—surface texture: Profile method—rules and procedures for the assessment of surface texture. International organization for standardization.

3. Abukhshim N., Mativenga P., Sheikh A. (2006) Heat generation and temperature prediction in metal cutting: A review and implications for high speed machining. International Journal of Machine Tools and Manufacture 46(7–8): 782–800

4. Baradie M. E. (1996) Cutting fluids: Part i. Characterisation. Journal of Materials Processing Technology 56: 786–797

5. Biermann D., Kersting M., Kessler N. (2009) Process adapted structure optimization of deep hole drilling tools. CIRP Annals Manufacturing Technology 58(1): 89–92

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