Abstract
The grinding process is inherently characterized by high specific energy and high temperature, which are responsible for high wheel loading and rapid wheel wear because of increasing grinding forces and reduced metal-removal rate.This paper describes a possibility of reducing coolant used, and a possible method instead. Finally a neural network models was constituted and tested by field data for the grinding.The experimental result indicates that the specific grinding force, the specific grinding energy and burning decreased under mist-cooling, cryo-mist-cooling. Cryo-mist-cooling also enables retention of the wheel sharpness for a longer period and result in less force and specific energy being required, but it does not suit to mirror finish. On the other hand, it is possible for oil mist.Based on the experiment, using the neuro network to predict grinding process.