Prediction of grinding machinability when grind aluminium alloy using water based zinc oxide nanocoolant
This thesis deals with the prediction of grinding machinability when grind aluminium alloy using water based zinc oxide nanocoolant. The objective of this thesis is to find the optimum parameter which was the depth of cut, investigate the surface roughness and wear produced during experimental and d...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2012
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| Online Access: | http://umpir.ump.edu.my/id/eprint/4633/ http://umpir.ump.edu.my/id/eprint/4633/ http://umpir.ump.edu.my/id/eprint/4633/1/cd6648_85.pdf |
| Summary: | This thesis deals with the prediction of grinding machinability when grind aluminium alloy using water based zinc oxide nanocoolant. The objective of this thesis is to find the optimum parameter which was the depth of cut, investigate the surface roughness and wear produced during experimental and develop the prediction model with the usage of Artificial Neural Network (ANN). The work piece used was aluminium alloy and zinc oxide nanocoolant as the grinding coolant. The grinding process was carried out with the usage of silicon carbide as the grinding wheel. The design of experiment was nine experiments for each single and multi-pass. The parameter used in this study was various depth of cut. The thesis describes the effect of coolant on the surface roughness and also the wheel wear. As a result, the usage of nanocoolant lead to the decrease in the surface roughness and also the wheel wear. The 2D microstructure of the grinded material was observed to view the material condition for various depth of cut. The surface roughness for grinding process using nanocoolant has a better result compared to water based coolant. Next, the result was trained using ANN to develop the prediction model for various depth of cut. Basically, the surface roughness became constant at one point with the increasing of depth of cut, whereby plastic deformation occurs. To conclude this study, the objective of the study was achieved, 1) the optimum depth of cut was 5µm, 2) the surface roughness of the material was investigated, whereby the roughness increase with the increasing of depth of cut and 3) the prediction model was done with ANN. As for the recommendation, the usage of different type of nanocoolant with various concentration and different particle sizes may affect the surface roughness of the material and also the wear produced. Next, the usage of different type and size of wheel should be considered in order to obtain a better surface finish. |
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