Determination of residual stresses and material properties by an energy-based method using artificial neural networks

H Jin,L Yan,W Yang

doi:10.3176/proc.2012.4.04

Abstract

With the help of an energy-based method and dimensional analysis, an artificial neural network model is constructed to extract the residual stress and material properties using spherical indentation. The relationships between the work of residual stress, the residual stress, and material properties are numerically calibrated through training and validation of the artificial neural network (ANN) model. They enable the direct mapping of the characteristics of the indentation parameters to the equi-biaxial uniform residual stress and the elastic–plastic material properties. The proposed ANN can quickly and effectively predict the residual stress and material properties based on the load–depth curve of spherical indentation.

Highlights

With the help of an energy-based method and dimensional analysis, an artificial neural network model is constructed to extract the residual stress and material properties using spherical indentation
They enable the direct mapping of the characteristics of the indentation parameters to the equi-biaxial uniform residual stress and the elastic–plastic material properties
The proposed artificial neural network (ANN) can quickly and effectively predict the residual stress and material properties based on the load–depth curve of spherical indentation

Summary

INTRODUCTION

Determining the residual stress is crucial in a wide variety of systems because residual stress in materials and structures is associated with fatigue, corrosion, wear, and failures of the systems. Comparison with the results of conventional saw-cutting tests showed that the indentation test could be effectively and used for the assessment of residual stress Their methods for residual stress determination require an unstressed reference sample and the residual indentation area needs to be measured. They pointed out that this technique is well suited for the mapping of residual stresses over the surface of a component because nanoindentation involves investigation of relatively small volumes of material They argued that using hardness as a measured parameter to determine residual stress will cause difficulties, since the sensitivity of hardness is lower and less consistent because the influence of pileup on the area of contact is larger. The residual stress and material properties can be extracted from indentation curves using the ANN approach

THEORETICAL ANALYSIS

FINITE ELEMENT SIMULATION

ARTIFICIAL NEURAL NETWORKS

RESULTS AND DISCUSSION

CONCLUSIONS

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