Study On Machined Surface Integrity Of Large Precision Gear Ring

Large precision gear ring, as one of the most important basic component in large-scale CNCmachine-tools which can machine large-scale structures, is a key component to affect such factors asvibration, noise, inherent gap, wear, etc. of their transmission systems. So, it will affect greatly theprecision of CNC machine-tools. The surface integrity has direct influence on the performance of alarge gear ring, and how to improve the surface integrity is a key problem in machining the large gearring. In this paper, the surface integrity of the gear teeth milled and ground is investigatedexperimentally. The main work and results are as follows:1. The influence of machining parameters on the spindle current of milling and surface quality ofgear ring was investigated by milling experiments. The results show that in the experiment condition,cutting speed has the most influence on the surface roughness, and the feed speed also has infect onthe surface roughness. The machining parameters have no significant effect on the tooth surfacemicrohardness. Feed speed has the most influence on the surface residual stress of the tooth, also thecutting speed has the effect on the surface residual stress.2. The influence of gear milling of two different ways has been studied by the millingexperiments. The results show that in the same experiment condition, the tool wear happens morefrequently in way of down milling. Two milling ways have no differ significant effect on the surfacehardening and residual stress.3. The influence of machining parameters on the surface roughness was analysed by grindingexperiments. Optimal parameter for surface roughness were also given. Surface topography andmicorstructure of gear grinding were analyzed. Moreover, the influence of machining parameters onthe tooth hardening and the residual stress were studied. The results show that the low surfaceroughness, work hardening and residual compressive stress can be obtained by gear grinding.4. Prediction model of surface roughness of the tooth under the condition of gear grinding wereset up by BP neural network prediction. The prediction results show that the relative error between thepredicted and measured values is small, so the BP neural network prediction model can well predictthe values of the machined surface roughness.