Experimental investigation on CBN turning of hardened AISI 52100 steel

By Y. Chou, Christabel Evans, M. Barash. 2002

165 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/f6fae93fe49fb74f72c29de04fe86dd3ec9ec97e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanical Engineering

1. Tugrul Özel
2. Yigit Karpat
3. Y. Chou
4. Yong Huang
5. S. Liang
6. Y. Şahin
7. Liangchi Zhang
8. J. A. Arsecularatne

In Mechanics of Materials

1. Y. Chou
2. Yong Huang
3. S. Liang
4. Tugrul Özel
5. Yigit Karpat
6. Y. Şahin
7. J. A. Arsecularatne
8. Liangchi Zhang

Citations in the corpus

• Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method (2011, background): “Chou, Evans, and Barash (2002) studied the performance and wear behavior of different cubic boron nitride (CBN) tools in finish turning of hardened AISI 52100 steel.”
• CBN tool wear in hard turning: a survey on research progresses (2007, methodology): “5 A simple model of adhesive wear process with the interaction of a transferred layer [42] 448 Int J Adv Manuf Technol (2007) 35:443–453”
• Wear and tool life of tungsten carbide, PCBN and PCD cutting tools (2006, background): “plain carbon steels), PCBN-L tools show lower wear rate than PCBN-H [29,35,36].”
• Machinability investigations on hardened AISI 4340 steel using coated carbide insert (2012, unknown): “[38,39], Thiele et al.”
• Comparison of tool life between ceramic and cubic boron nitride (CBN) cutting tools when machining hardened steels (2009, background): “Similar results were obtained with previous works carried out by Lima et al.(2005), Jang et al.(2000); Sahin (2006), Benga and Abrao (2003), Chou et al.(2002), Yang and Tarng (1998), Davim and Figueira (2006).”
• On machining of hardened AISI D2 steel with PCBN tools (2006, background): “Despite the aforementioned advantages of hard turning of steels compared to grinding, there is a clear need for further research to clarify the issues in the areas of tool wear/life, surface integrity, work piece quality, process reliability and process modelling [1–3].”
• Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks (2005, methodology): “…of response is a residual e Z y K ^ y (10) This regression analysis technique using least squares estimation was applied to compute the coefficients of the exponential model by using the sparse experimental data generated by Chou et al. [13] for hard turning of AISI 52100 steel using CBN tools.”