Pre-evaluation on surface profile in turning process based on cutting parameters-Reference-Cited by-同舟云学术

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Pre-evaluation on surface profile in turning process based on cutting parameters

Published:2009-11-20 Issue:5-8 Volume:49 Page:447-458
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Lu Chen,Ma Ning,Chen Zhuo,Costes Jean-Philippe

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

http://link.springer.com/content/pdf/10.1007/s00170-009-2417-9.pdf

Reference32 articles.

1. Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844

2. Grzesik W (1996) A revised model for predicting surface roughness in turning. Wear 194:143–148

3. Chang CK, Lu HS (2006) Study on the prediction model of surface roughness for side milling operations. Int J Adv Manuf Technol 29:867–878

4. Chen C-CA, Liu W-C, Duffie NA (1998) A surface topography model for automated surface finishing. Int J Mach Tools Manuf 38:543–550

5. Ehmann KF, Hong MS (1994) A generalized model of the surface generation process in metal cutting. CIRP Annal 43:483–486

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Energy efficiency identification and surface roughness prediction using cutting force signal for computer numerical controlled machine systems;Scientific Reports;2024-08-16

2. Predicting surface roughness in machining aluminum alloys taking into account material properties;International Journal of Computer Integrated Manufacturing;2024-07-05

3. A Method to Design Profiled Cutting Tools for Inner Turning;Symmetry;2022-12-19

4. Remote Monitoring for Surface Roughness Based on Vibration and Spindle Power;Arabian Journal for Science and Engineering;2022-05-11

5. Feature selection and domain adaptation for cross-machine product quality prediction;Journal of Intelligent Manufacturing;2021-11-23

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