Study On The Experiment Of Microcrystalline Composite Materials With AWJ Cutting

Abrasive water jet cutting is a green cold cutting,the advantage of high efficiency,relatively smooth incision,a wide range of processing,less pollution,more and more people have been appreciated in all walks of life have been Widely used.Due to the different nature of the different materials and the removal mechanism of large differences,and the current domestic research in this area is not yet mature,limiting the abrasive water jet technology promotion and application.Therefore,it is necessary to study the processing mechanism and process parameters of abrasive water jet.In this paper,the structure of abrasive water jet,the mixing mode of abrasive and water and the mixing process are discussed.The formation mechanism of each section of cutting section is analyzed.At the same time,the effect of abrasive water and water on the removal of water jet Systematic analysis.The effects of four parameters,such as jet pressure,target distance,cutting speed and abrasive flow rate,on the maximum cutting depth and roughness were investigated,and the influencing factors were analyzed.The results show that the effect of abrasive water jet cutting microcrystalline composite material.Through the orthogonal test,the results of the test range and variance analysis of variance,obtained the parameters of the maximum cutting depth,cutting efficiency and roughness of the impact of the order and degree of prominence.On this basis,the gray theory was used to optimize the multi-index parameters of abrasive water jet cutting microcrystalline composites to single-index optimization,so as to find the combination of process parameters which can take the above three indexes into consideration.According to the results of orthogonal test,the regression model of maximum cutting depth,cutting efficiency and surface roughness and the comprehensive regression model of the three were established by using spss software and gray theory respectively,and the model was validated for practical production and scientific research.Activities in the process indicators to predict and select the parameters of good or bad to judge.