Effect of Cutting Fluid on Diamond Tool Life under Micro V-Groove Turning of Cobalt-Free Tungsten Carbide

Micro-machining of hard dies and molds for optical parts or precision instruments is required to extend die and mold life. This paper investigates the effect of cutting fluid on diamond tool life under micro V-groove turning of cobalt-free tungsten carbide. Zinc dialkyldithiophosphate fluid (ZnDTP) displayed excellent diamond tool wear resistance in previous experiments. The performance of this cutting fluid is compared to newly developed vegetable oil based cutting fluid with dispersed MoS2 nanotubes. This paper investigates nanopolycrystalline diamond (NPD) tool life with a rake angle of 0° and-30° under continuous micro V-groove turning (i.e. face turning), of cobalt-free tungsten carbide using the developed cutting fluids. Superior diamond tool edge wear resistance is observed when using the dispersed MoS2 nanotubes in vegetable oil and using a NPD tool with a-30° rake angle.

Preliminary evaluation of the applicability of F, V and AEsignals in diagnosis of ADI machining process

In this study, a preliminary evaluation was made of the applicability of the signals of the cutting forces, vibration and acoustic emission in diagnosis of the hardness and microstructure of ausferritic ductile iron and tool edge wear rate during its machining. Tests were performed on pearlitic-ferritic ductile iron and on three types of ausferritic ductile iron obtained by austempering at 400, 370 and 320°C for 180 minutes. Signals of the cutting forces (F), vibration (V) and acoustic emission (AE) were registered while milling each type of the cast iron with a milling cutter at different degrees of wear. Based on individual signals from all the sensors, numerous measures were determined such as e.g. the average or maximum signal value. It was found that different measures from all the sensors tested depended on the microstructure and hardness of the examined material, and on the tool condition. Knowing hardness of the material and the cutting tool edge condition, it is possible to determine the structure of the material .Simultaneous diagnosis of microstructure, hardness, and the tool condition is probably feasible, but it would require the application of a diagnostic strategy based on the integration of numerous measures, e.g. using neural networks.