Study on Prediction Model for Cutting Surface Roughness of Hardened GCr15 Bearing Steel

Ting Yang; Hong Bin Liu; Hai Tao Wu

doi:10.4028/www.scientific.net/AMR.753-755.298

Paper Titles

Study on Action Mechanism and Application of the Precision Carbide Cutting Cutter Edge
p.271

Study Progress on Grinding Surface Residual Stresses for Nano-Ceramic Coatings
p.277

Grinding-Hardening Experiments and Grinding Force Analysis in Infeed External Grinding for 45 Steel
p.281

Study on Impact Stress Characteristics in Ultrasonic Cutting and Brittle Material Removal Mechanism
p.287

Study on Prediction Model for Cutting Surface Roughness of Hardened GCr15 Bearing Steel
p.298

A Summarization of Tribological Behaviors of Self-Lubricating Composite Ceramic Tool
p.302

Research on Relationship of Residual Stress, Grinding Conditions and Workpiece Property in Engineering Ceramic Grinding Surface
p.306

The Application and Research of Minimum Quantity Lubrication Machining
p.310

Progress of Traditional Ultra-Precision Machining Processes
p.314

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755Study on Prediction Model for Cutting Surface...

Study on Prediction Model for Cutting Surface Roughness of Hardened GCr15 Bearing Steel

Abstract:

CBN cutting tool is used to cut hardened GCr15 bearing steel. Based on the surface roughness under different cutting parameters, people built the prediction model of surface roughness by Linear Regression algorithm, and carried on the significance test of it, the results show that the surface roughness forecast model set up by linear regression algorithm has a certain reliability and its linear regression equation is significant; among the cutting parameters, cutting speed and feed rate make great impact to the surface roughness and back engagement of the cutting edge has little impact to the surface roughness of test specimen.

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Periodical:

Advanced Materials Research (Volumes 753-755)

Pages:

298-301

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.753-755.298

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Online since:

August 2013

Authors:

Ting Yang, Hong Bin Liu, Hai Tao Wu

Keywords:

CBN Tool, Cutting Parameter, Linear Regression Algorithm, Surface Roughness (SR)

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