Prediction of the Material Removal Rate and Surface Roughness in Electrical Discharge Diamond Grinding Using Best-Suited Artificial Neural Network Training Technique
- 19 September 2018
- book chapter
- Published by Springer Nature
- p. 487-495
Abstract
No abstract availableKeywords
This publication has 8 references indexed in Scilit:
- Manufacturing of dies from hardened tool steels by 3-axis micromillingProduction Engineering, 2011
- Mechanical properties of cBN–Al composite materialsCeramics International, 2011
- Spanen2011
- Empirical modeling of hard turning of AISI 6150 steel using design and analysis of computer experimentsProduction Engineering, 2010
- Geometry of Single-point Turning Tools and Drills2010
- Mechanics of high speed cutting with curvilinear edge toolsInternational Journal of Machine Tools and Manufacture, 2008
- The effect of temperature on the hardness of polycrystalline cubic boron nitride cutting tool materialsInternational Journal of Refractory Metals and Hard Materials, 2004
- Superabrasives and Ultrahard Tool Materials1990