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The Experimental Analysis Of Magnetic Abrasive Finishing Machining Based On Grey Correlation

Posted on:2015-05-01Degree:MasterType:Thesis
Country:ChinaCandidate:P K HouFull Text:PDF
GTID:2181330434458522Subject:Mechanical engineering
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This research topic is root in the national natural science fund project (51175365)-"The abrasive performance characteristics and index system research of the magnetic abrasive finishing machining". This fund project studied the preparation process and method of the magnetic abrasive mainly, explored the magnetic abrasive’s machining features, and establishd the comprehensive evaluation system of the magnetic abrasives.The magnetic abrasive finishing technology as a method of surface finishing, because of its unique processing characteristics, the magnetic abrasive has a favourable adaptability when machining workpiece surfaces. So the magnetic abrasive finishing machining technology got a great development, and applied to production field gradually. However, the magnetic abrasive as the grinding apparatus of this finishing technology, its performance play an important role to the parts’surface quality.This article chooses the binding process of magnetic abrasive as the research object, this method of preparation abrasive not only have the low preparation cost, but also the preparation technology is simple. By means of the magnetic abrasive performance, binding theory and binding process were analyzed, made the preparation process of mixing, binding, solidification, smashing and screening, and prepared the magnetic abrasive with alumina as the abrasive particle, and carry on an experimental research of the45#steel.At present, the common use data statistical approach have variance analysis and regression analysis, but this analytical methods need a large amount of datas as support, not only has a large computational complexity, but also the distribution regularity between factors are often uncertain. The analysis with mathematical statistics method has some uncertainty, so this article try to use grey correlation analysis method to research the magnetic abrasive finishing machining process parameters. This method not only has a small amount of calculation, but also has the outstanding advantages when analysis the datas of uncertain distributing rule.This article is based on the research of magnetic abrasive finishing machining method, choose electric current, speed of main shaft, machining gap, processing time as the technological parameters which affect the technic index, choose the surface roughness as technic index,and designed the orthogonal experiment, put forward the grey theory to be applied to analyze the experiment results. And combined with grey correlation analysis method, and concluded the technological parameters’influence rule and influence extent on the technic index. Using the grey decision theory, and the optimization analysis has been used to the experiment of orthogonal design, finally concluded the optimal parameter combination. After determined the optimal technic parameter combination, applied the grey theory to research the process parameters of magnetic abrasive preparation. Choose iron powder mesh number, the ratio of iron powder and abrasive powder the broken mesh as the preparation parameters which affect the technic index, and also choose the surface roughness as technic index, and design the orthogonal experiment. And combined with grey correlation analysis method, and concluded the preparation parameters’influence regard to the technic index. Using the grey decision theory, and the optimization analysis has been used to the experiment, concluded the optimal parameter combination.In this paper, the research will contribute to the widely application of the grey system theory, at the same time provides a theoretical reference basis about the selection of the magnetic abrasive finishing machining process parameters and the bonded abrasive’s preparation parameters.
Keywords/Search Tags:magnetic abrasive, finishing machining, binding process, thegrey theory, orthogonal experiment, parameter optimization
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