Cutting Capability Assessment of Highly Porous CBN Wheels by Microrelief of Plane Parts from 06Cr14Ni6Cu2MoWÐ¢i-Sh Steel Using Artificial Intelligence System

Yakov Iosifovich Soler; Nguyen Manh Tiem; Nguyen Van Le

doi:10.5614/j.eng.technol.sci.2016.48.4.7

Authors

Yakov Iosifovich Soler Technology and Equipment of Mechanical Manufacture Department, Irkutsk National Research Technical University, 664074, Irkutsk, Lermontov St., Bldg. 83
Nguyen Manh Tiem Technology and Equipment of Mechanical Manufacture Department, Irkutsk National Research Technical University, 664074, Irkutsk, Lermontov St., Bldg. 83
Nguyen Van Le Technology and Equipment of Mechanical Manufacture Department, Irkutsk National Research Technical University, 664074, Irkutsk, Lermontov St., Bldg. 83

DOI:

https://doi.org/10.5614/j.eng.technol.sci.2016.48.4.7

Abstract

The abrasive tools are the weakest element in the grinding process system, to which great attention is being paid by scientific and industrial collectives. Eleven highly porous wheels (HPWs) were tested: CBN30 (B76, B107, B126, B151) 100 OVK27-F40; CBN30 B107 100 OVKC10-F40; CBN30 B126 100(M, L) VK27- (F25, F40); LKV50 (B107, B126) 100 (M, O) VK27-F40. Assessment of the surface topography was carried out by roughness parameters R_a, R_max, and S_m(GOST 25472-82), which were considered random variables with their position and dispersion measures. Two artificial intelligence systems "? fuzzy logic (FL) and neural networks (NN) "? were used to analyze the HPW's cutting capability (CC). In both cases, the best CC was predicted for grinding with CBN30 (B76 and B151) 100 OVK27-F40 and LKV50 (B107) 100 OVK27-KF40. In the absence of a training process in FL modeling, the assessments for the wheels with a low CC were less reliable.

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