Acquisition of Metal Plastic Parameters Based on Neural Network Learning and Residual Indentation Morphology
Compared to conventional mechanical testing methods,the indentation method offers the advantages of simple manufacturing of samples and in-situ testing.This study proposes an alternative to deriving material mechanical parameters solely from indentation load-depth curves.It introduces an effec-tive method for deducing metal plastic mechanical parameters based on residual indentation morphology and neural network learning.An Instron universal material testing machine was used to conduct spherical in-dentation tests on Cu,Mg,and Fe,followed by scanning their residual indentation morphology through the contour morphology system.The extracted morphology features served as the basis for further analy-sis.Data processing techniques such as amplification,rounding,binarization,and high-order digit supple-mentation were applied to the acquired data.Through Abaqus software and numerical simulations,residu-al indentation depth data associated with various material parameters were automatically extracted for neu-ral network learning.Selections of activation function,neural network parameter initialization and upda-ting mode,loss function,parameter optimization strategy,and neural network structure were carefully conducted to ensure effective learning.The plastic mechanical parameters of Cu,Mg,and Fe were ob-tained based on the residual indentation morphology feature data from indentation tests and the neural net-works after learning.Additionally,the related plastic mechanical parameters of Cu,Mg,and Fe were also acquired through conventional uniaxial tensile tests and characterization using the Instron machine.By comparing the neural network learning results with tensile test data,relative errors in plastic mechanical parameters were identified.The effectiveness of the proposed method in obtaining metal plastic mechanical parameters based on neural network learning and residual indentation morphology was validated.This method can be expanded for characterizing mechanical properties and acquiring plastic parameters of other metal/alloy materials.
万方数据
维普
