The writing of scientific papers can't be done with charts showing important data, and graphically showing the relationship between material attributes. It not only highlights important relationships, but also enhances understanding of material behaviors and facilitates selection of materials. However, in many cases, these correlations are highly multidimensional in nature, and it is usually only possible to use two-dimensional graph analysis to establish relationships between different attributes and may only express certain aspects of these relationships. It is difficult to make the material's high-dimensional data visible and meaningful comparative analysis possible. Jeffrey Rickman from Lehigh University in the United States used visual analysis (parallel coordinate) data analysis methods to better demonstrate multidimensional material data and better identify useful relationships between different attributes. In this way, he constructed a multi-dimensional material property diagram for metal and ceramic materials and systematically analyzed it, simplified the description of high-dimensional geometric figures, realized the relationship between size reduction and important attributes, and strengthened different materials. The distinction between categories provides a powerful tool for identifying the relationships between various attributes of materials.
å•è¯, is often beneficial to display material properties relationships in the form of charts that highlight important correlations and occur enhance our understanding of materials behavior and facilitate materials selection. unfortunate, in many cases, these correlations are highly multidimensional in nature, and one typically employs low -dimensional cross-sections of the property space to convey some aspects of these relationships. To overcome some of these difficulties, in this work we employed methods of data analytics in conjunction with a visualization strategy, known as parallel coordinates, to represent better multidimensional materials The data charts for the description of high-dimensional Of significant property correlations and underline distinctions among different materials classes.
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