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Prediction of grinding machinability when grind aluminium alloy using water based zinc oxide nanocoolant

机译:水基氧化锌纳米冷却剂磨削铝合金时的磨削加工性预测

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摘要

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.
机译:本文主要研究了水基氧化锌纳米冷却剂磨削铝合金时的磨削加工性能。本文的目的是找到最佳参数,即切削深度,研究实验过程中产生的表面粗糙度和磨损,并利用人工神经网络(ANN)开发预测模型。所使用的工件是铝合金和氧化锌纳米冷却液作为研磨冷却液。研磨过程是使用碳化硅作为砂轮进行的。实验的设计是每个单次和多次通过9个实验。本研究中使用的参数是各种切削深度。本文描述了冷却液对表面粗糙度以及车轮磨损的影响。结果,纳米冷却剂的使用导致表面粗糙度的减小以及车轮磨损的减小。观察研磨材料的二维微观结构,以观察各种切削深度的材料状况。与水基冷却剂相比,使用纳米冷却剂进行研磨过程的表面粗糙度更好。接下来,使用ANN对结果进行训练,以开发针对各种切削深度的预测模型。基本上,随着切削深度的增加,表面粗糙度在某一点变得恒定,从而发生塑性变形。结束本研究,达到了研究的目的,1)最佳切割深度为5μm,2)研究了材料的表面粗糙度,由此粗糙度随切割深度的增加而增加,以及3)预测模型是用ANN完成的。根据建议,使用具有不同浓度和不同粒径的不同类型的纳米冷却剂可能会影响材料的表面粗糙度以及所产生的磨损。接下来,应考虑使用不同类型和尺寸的砂轮以获得更好的表面光洁度。

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    Suganthi Jayaraman;

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  • 年度 2012
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