Clustering Regression Modeling for Gear Hobbing Machine Thermal Error Compensation

Qian Jian Guo; Xiao Ni Qi

doi:10.4028/www.scientific.net/KEM.416.401

Paper Titles

Study of Inhibition Function of Grinding Fluid Additive to Leaching Cobalt from Cemented Carbide
p.381

Study on Performance of Hot Pressed High Phosphorus-Boron Iron-Based Bit Matrix and Diamond Bit
p.386

Study of Magnetic Finishing for an Internal Surface Using a Permanent Magnetic Pole
p.391

Experimental Research on Laser Brazing of Diamond Grits with a Ni-Based Filler Alloy
p.396

Clustering Regression Modeling for Gear Hobbing Machine Thermal Error Compensation
p.401

Study of Finishing Mechanism for Internal Surface Using Magnetic Force Generated by Rotating Magnetic Field
p.406

Theoretical Research and Verification on the Porosity of Semi-Fixed Abrasive Tool
p.411

Research on the Basic Properties of the Bamboo Charcoal Bonded Grinding Wheel
p.416

The Development of Large High Efficient CNC Gantry Grinder for Vessel Head in Nuclear Power
p.421

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Clustering Regression Modeling for Gear Hobbing Machine Thermal Error Compensation

Abstract:

This paper proposes a new thermal error modeling methodology called Clustering Regression Thermal Error Modeling which not only improves the accuracy and robustness but also saves the time and cost of gear hobbing machine thermal error model. The major heat sources causing poor machining accuracy of gear hobbing machine are investigated. Clustering analysis method is applied to reduce the number of temperature sensors. Least squares regression modeling approach is used to build thermal error model for thermal error on-line prediction of gear hobbing machine. Model performance evaluation through thermal error compensation experiments shows that the new methodology has the advantage of higher accuracy and robustness.