Increasing critcal depth of cut in ductile mode machining of tungsten carbide by process parameter controlling

Ductile mode machining is usually applied for the optical finishing operation of e.g. tungsten carbide molds. One request for this mode is not to exceed the critical depth of cut hcu,crit characterized by the transition point from ductile to brittle material removal. Based on experimental investigations a formula for the critical depth of cut, relating the material specific properties Young’s-Modulus E, material hardness H and fracture toughness KC was developed by Bifano et. all [1]. Even when the influence of cutting conditions, like tool or process characteristics, are neglected the formula is widely used for setting up UPM machines ever since. However, previous investigations have shown that hcu,crit strongly depends on coolant fluid characteristic as well as on the compressive stress applied into the cutting zone by the use of tools with e.g. negative rank angles [2]. In this paper, we report on a ductile process analysis applying a recently developed method for process optimization in optics fabrication [3]. Following that trail, critical process parameters have been identified and their influences on the critical depth of cut hcu,crit have been tested experimentally in fundamental ruling tests. Among others, following parameters were identified and tested: (a) characteristics of the coolant used, (b) the pH value of the coolant, (c) the tool specifications of the applied diamond and (d) whether ultrasonic assistance (US) is being switched on or off. Depending on the applied set of process parameters and for the experimental data collected, maximum ductile mode material removal rates could be achieved with dcmax = 1600 nm. That way, a new formula was developed, which allows the prediction of the critical depth of cut depending on critical process parameters while machining binderless nanocrystalline tungsten carbide. The formula was set up based on experimental results and is one step towards extending Bifanos formula taking the influences of critical process parameters into account.