The Process Experimental Study On High Efficiency Deep Grinding For TC4 Titanium Alloy And Its Prediction Of Grinding Quality

Titanium alloy are becoming popular materials in many areas for their highperformances such as high specific strength, good corrosion resistance, antimagneticcapability, high thermal endurance and fatigue resistance. But their highperformances also makes them difficult to grind. It will make some problems such ashigh abrasive force and grinding temperature, adherence, bad wearance, if somegeneral ways has been used to grind titanium alloy. So, new advanced grindingmethods are needed urgently to improve machining productivity and grindingsurface integrality.High efficiency deep grinding (HEDG) refers to an advanced modern grindingprocess, which utilizes the high or ultra-high grinding speeds to achieve theunimaginable high grinding efficiency, high grinding ratio and also satisfactorysurface quality. It has brought the traditional grinding technology to a revolutionarystage and revealed its deep hidden developing possibilities and prosperousapplication vista. It has great help about HEDG in application to the grinding oftitanium alloy for widening its range of application and searching new efficiencyways to grinding titanium alloy. Based on the experiments about high efficiencydeep grinding 40Cr steel, systemic experiments was done about grinding TC4titanium alloy under the high efficiency deep grinding condition. In the experimentsthe grinding force and workpiece surface roughness of two materials in the conditionof HEDG were measured by change the grinding conditions. The author discussedthe effect of various parameters on grinding force, specific grinding energy, grindingforce ratio, studied the indicated material removal modes and force formationmechanisms, also develop the mathematical model of the high efficiency deepgrinding force of the TC4 titanium alloy in the condition of adherence. Then,theresearch about the characteristics, formation of specific grinding energy and theconsumption of grinding power was carried out. At last, the prediction model ofworkpiece surface roughness was made by using traditional BP neural network andevolutionary neural networks. The results of using different ways was analyzed andcompared.The results indicated that the grinding conditions have great effect to unit areagrinding force and unit area grinding force, specific grinding energy corresponding to the maximum undeformed chip thickness and the equivlent cutting thickness well.The removal modes of two materials are mainly plastic and the materials areremoved by gliding and ploughing when the great grinding depth and high workpiecevelocity are applied. There has difference in gringing force and specific grindingenergy because of their mechanical properties. Diamond wheel can be used to highefficiency deep grind TC4 titanium alloy, but CBN wheel is much better in grindingit. The grinding force model of built by G. Werner is also prevalent in the TC4titanium alloy high efficiency deep grinding. The results also indicated that grindingpower per unit width monotonously increases with the grain plough area per unitwidth. Evolutionary neural networks based genetic algorithm is obviously better thantraditional BP neural network in the field of convergence rate and accuracy ofpredictions. It can effectively overcome the problem of falling into local minimumpoint and have great use value in the engineering.