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Contact usually results in stress concentration which can easily cause the yield of materials and structures. The classic elastic–plastic yield criterion needs to utilize stress or strain field for calculation. However, most advanced full-field measurement methods output the displacement as the original data, and the fitting from displacement to strain will induce error accumulation in applications. In this paper, a plastic domain characterization method is developed that can directly judge the elastic–plastic state of materials based on the full-field displacement and neural network. By establishing and training a three-layer-based neural network, the relationship between the displacement and the elastic/plastic stage of the sampling points is modeled. A physical model is formulated based on the yield criterion and embedded in the layer of the network, which can increase the convergence rate and accuracy. Only the displacements of the contact member are required in this method, which can be easily measured by the optical metrologies. The performances of the developed method are carefully discussed through simulated data and real-world tests. Results show that the method can accurately identify the plastic domain during the tests.

The processing method has an effect on the morphology and yield strength of the rough surface. In this paper, experiments and finite element methods are used to analyze the contact behavior of asperities on the rough surface. And the effects of the distribution, height, surface noise, and yield strength of the asperities on the rough surface on the plastic expansion of the asperities are discussed. For the rough surface of the aluminum alloy machined by the three processing methods, the plastic evolution of the surface asperities is directly observed in the experiment. The results show that when the surface roughness is lower, the plastic growth of various asperities on the surface is more uniform. We consider that the morphology of the asperities consists of the periodic morphology and the noise of this morphology. The plastic deformations of rough surfaces with different morphologies are analyzed by finite element method. The results show that the higher the frequency of the asperity distribution, the smaller the plastic deformation under the same load. And the plastic deformation of asperities increases with height. In addition, the mean value of the noise has a limited effect on the plastic change of the rough surface. However, when the noise variance gradually increases, the plastic area of the asperities shows a slow upward trend.