Analysis of deep mining model for indentation data of biomaterials

Abstract

The traditional data mining model of indentation has low accuracy in analyzing the linear relationship between the relevant physical quantities of the indentation, so a deep mining model for indentation data of biomaterials is designed. Firstly, the constitutive relation of the material is set by the actual indentation, and the dimension data are collected by the independent free variable function. The characteristic Raman peak is obtained according to the properties of the biological nanomaterials. The stress data are preprocessed by selecting the direction of indentation, which is convenient to observe the dislocation nucleation and deformation twin phenomenon in the process of indenting. The synergistic effect of these dislocations leads to the fact that the load displacement curve shows obvious linear relationship, so as to complete the analysis of the deep mining model of the indentation data of biological nanomaterials. The experimental results show that in the linear relationship analysis of contact depth and indentation depth, the linear relationship discreteness of the designed model is 0.44 lower than that of the traditional model and in the linear relationship analysis of contact stiffness and indentation depth, the linear relationship discreteness of the designed model is 0.38 lower than that of the traditional model, which indicates that the accuracy of the designed model is higher than that of the traditional model in analyzing the linear relationship between the relevant physical quantities of the indentation. In addition, the average accuracy of the model for five different materials is 98.23%.