Hard machining of hardened bearing steel using cubic boron nitride tool |
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Authors: | Mohamed Athmane Yallese Kamel Chaoui Nassereddine Zeghib Lakhdar Boulanouar Jean-François Rigal |
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Affiliation: | 1. Department of Mechanical Engineering, Faculty of Science and Technology, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk Ahras 41000, Algeria;2. Laboratory of Mechanics and Structures (LMS), 8 May 1945 University, P.O. Box 401, Guelma 24000, Algeria |
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Abstract: | In many cases, hard machining remains an economic alternative for bearing parts fabrication using hardened steels. The aim of this experimental investigation is to establish the behaviour of a CBN tool during hard turning of 100Cr6-tempered steel. Initially, a series of long-duration wear tests is planned to elucidate the cutting speed effects on the various tool wear forms. Then, a second set of experiments is devoted to the study of surface roughness, cutting forces and temperature changes in both the chip and the workpiece. The results show that CBN tool offers a good wear resistance despite the aggressiveness of the 100Cr6 at 60HRC. The major part of the heat generated during machining is mainly dissipated through the chip. Beyond 280 m/min, the machining system becomes unstable and produces significant sparks and vibrations after only a few minutes of work. The optimal productivity of machined chip was recorded at a speed of 120 m/min for an acceptable tool flank wear below 0.4 mm. Beyond this limiting speed, roughness (Ra) is stabilized because of a reduction in the cutting forces at high speeds leading to a stability of the machining system. The controlling parameter over roughness, in such hard turning cases, remains tool advance although ideal models do not describe this effect rationally. Surface quality obtained with CBN tool significantly compared with that of grinding despite an increase in the advance by a factor of 2.5. A relationship between flank wear (VB) and roughness (Ra) is deduced from parametric analysis based on extensive experimental data. |
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