journal article·2018-03

Suppression of diamond tool wear in machining of tungsten carbide by combining ultrasonic vibration and electrochemical processing

Xinquan Zhang, Rui Huang, Kui Liu, A.Senthil Kumar, Hui Deng

Ceramics International· Volume 44· Issue 4
Publisher page

Abstract

Abstract As an important ceramic material, tungsten carbide (WC) is utilized as the typical mold in precision glass molding, which has replaced conventional grinding and polishing to provide a highly replicative process for mass manufacturing of optical glass components. Ultra-precision grinding, which is time consuming and has low reproducibility, is the only method to machine such WC molds to high profile accuracy. Although diamond turning is the most widely used machining method for fabrication of optical molds made of metals, diamond turning of WC is still considered challenging due to fast abrasive wear of the diamond tool caused by high brittleness and hardness of WC. Ultrasonic vibration cutting has been proven to be helpful in realizing ductile-mode machining of brittle materials, but its tool life is still not long enough to be utilized in practical diamond turning of optical WC molds. In the current study, a hybrid method is proposed to combine electrochemical processing of WC workpiece surface into the diamond turning process. Cutting tests on WC using poly-crystalline diamond tools were conducted to evaluate its effect on improvement of tool wear and surface quality. Validation cutting tests using single crystal diamond tools has proven that the proposed hybrid method is able to significantly reduce the diamond tool wear and improve the surface quality of machined ultra-fine grain WC workpiece compared to ultrasonic vibration cutting without electrochemical processing.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Surface Polishing Techniques · Advanced machining processes and optimization · Advanced Machining and Optimization Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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