Grinding is an often important finishing process for many engineering components\nand for some components it is even a major production process. In this study, prediction model\nhave been developed to find the effect of grinding condition in term of depth of cut and type of\ngrinding coolant. Zinc Oxide (ZnO) nano-coolant was used as a coolant with water as a based\nliquid. The experiments conducted with grinding depth in the range of 5 to 21μm. Silicon\nCarbide wheel are used to grind the AISI P20 tool work piece. Artificial intelligence model has\nbeen developed using Artificial Neural Network(ANN). Result shows that the lower surface\nroughness and wheel wear obtain at the lowest cutting depth which is 5 μm. Besides that, grind\nusing ZnO nano-coolant gives best surface roughness and minimum wheel wears compared to\ngrind using normal soluble coolant. The surface roughness have been reduced approximately\n47.84% for single pass experiment and 126.1% for multi pass experiment. However, there is no\nwheel wheel wear obtain for grinding using ZnO nanocoolant. From the prediction of ANN, it\ncan predict the surface roughness closely with the experimental value.

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