Modeling Surface Roughness and Hardness of Grinding SKD11 Steel Using Adaptive Network Based Fuzzy Inference

Chih Hsiang Chang; Jhy Cherng Tsai; Neng Hsin Chiu; Rei Yu Chein

doi:10.4028/www.scientific.net/AMR.126-128.171

Paper Titles

Development of Media for Low Pressure Abrasive Flow Machining
p.148

Experimental Investigation on High-Speed Grinding of 40Cr Steel with Vitrified CBN Grinding Wheel
p.154

On-Machine Method to Condition the Grinding Ability of Resin-Bond Wheels
p.159

A Study on the Spiral Polishing of the Inner Wall of Stainless Bores
p.165

Modeling Surface Roughness and Hardness of Grinding SKD11 Steel Using Adaptive Network Based Fuzzy Inference
p.171

Surface Texture Analysis of Fixed and Free Abrasive Machining of Silicon Substrates for Solar Cells
p.177

Influences of Characteristics of Large Particle on the ‘Trap’ Effect of Semi-Fixed Abrasive Tool
p.181

Laboratory Comparison of Mini-Discs with Point-Attack Picks
p.189

Tool Wear Mechanism in Continuous Cutting of Difficult-to-Cut Material under Dry Machining
p.195

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Modeling Surface Roughness and Hardness of Grinding SKD11 Steel Using Adaptive Network Based Fuzzy Inference

Abstract:

Grinding is a commonly used process in precision machining. This paper is aimed to establish a model for predicting surface roughness and hardness with respect to grinding parameters, including wheel speed, feed rate and depth of grinding for the SKD11 molding steel. The Adaptive Network based Fuzzy Inference (ANFIS) is used to construct the grinding model that is trained by experimental data from grinding the SKD11 steel. Test experiments are then conducted to verify the model. Results from the test experiments showed that the average error between surface roughness predicted by the model and the measured data is 3.94%. The average different of the hardness between the measured data and that predicted by the model is less than 0.06%. It verifies that the model can be used to predict surface roughness and hardness for different grinding parameters.