# Modeling and optimization of alloy steel 20CrMnTi grinding process parameters based on experiment investigation

Modeling and optimization of alloy steel 20CrMnTi grinding process parameters based on experiment... Three kinds of cylindrical plunge-grinding experiments are conducted with CBN grinding wheel by single-factor method for the carburizing alloy steel 20CrMnTi to investigate surface quality parameters and grinding force with change of wheel speed, workpiece speed, and the depth of cut. It is clarified that how change trends and correlations among surface quality parameters, grinding force, and grinding process parameters are. Based on the experiment results, analytical models for surface roughness and grinding force are established, which make the multi-object grinding parameter optimization possible and can predict the roughness and grinding force. The alloy steel 20CrMnTi workpiece surface quality and grinding efficiency-oriented optimization are conducted by the Strengthen PARETO algorithm, which make the prediction error good enough with roughness error less than 2% and the removal rate per unit width Q W ′ $${Q}_W^{\prime }$$ 1% below. The Strengthen PARETO optimal predictive model proves to be effective and sufficient to solve the problem of surface quality and grinding efficiency-oriented optimization. The alloy steel grinding parameters meet the requirement of the roughness under the value 0.8 μm with high grinding efficiency in automobile industry for shaft parts and gear production. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

# Modeling and optimization of alloy steel 20CrMnTi grinding process parameters based on experiment investigation

, Volume 95 (8) – Nov 16, 2017
15 pages

/lp/springer_journal/modeling-and-optimization-of-alloy-steel-20crmnti-grinding-process-8gTejxhGS1
Publisher
Springer Journals
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-1335-5
Publisher site
See Article on Publisher Site

### Abstract

Three kinds of cylindrical plunge-grinding experiments are conducted with CBN grinding wheel by single-factor method for the carburizing alloy steel 20CrMnTi to investigate surface quality parameters and grinding force with change of wheel speed, workpiece speed, and the depth of cut. It is clarified that how change trends and correlations among surface quality parameters, grinding force, and grinding process parameters are. Based on the experiment results, analytical models for surface roughness and grinding force are established, which make the multi-object grinding parameter optimization possible and can predict the roughness and grinding force. The alloy steel 20CrMnTi workpiece surface quality and grinding efficiency-oriented optimization are conducted by the Strengthen PARETO algorithm, which make the prediction error good enough with roughness error less than 2% and the removal rate per unit width Q W ′ $${Q}_W^{\prime }$$ 1% below. The Strengthen PARETO optimal predictive model proves to be effective and sufficient to solve the problem of surface quality and grinding efficiency-oriented optimization. The alloy steel grinding parameters meet the requirement of the roughness under the value 0.8 μm with high grinding efficiency in automobile industry for shaft parts and gear production.

### Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: Nov 16, 2017

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