Research On On-line Fatigue Crack Monitoring And Life Prediction Based On Strain Analysis

In mechanical products and equipment used in the fields of mechanical engineering and Aeronautics,its key components are often subjected to alternating loads such as heat and force,which are prone to wear,fracture,and fatigue damage,resulting in products to be discarded early in the effective life period.In order to ensure the safety and reliability of equipment,it is necessary to develop structural health monitoring technology.In this thesis,the fatigue crack propagation process of aluminum alloy materials was studied by numerical simulation and experiment.Then the pattern recognition of the crack length used the inverse data algorithm based on the model was proposed,and the residual life of the crack structure was predicted by the particle filter algorithm.Firstly,the fatigue crack growth process of the structure was simulated by ABAQUS finite element analysis software.The finite element modeling analysis of the aluminum plate experiment structure were carried out by ABAQUS software.The stress distribution cloud diagram of the model was obtained and the arrangement of the sensor was optimized.Using PYTHON scripting language for modeling analysis under ABAQUS platform,and the continuous crack expansion process was realized.Through the simulation,it was found that the strain trend was basically the same because of the tensile stress,and the strain increased with the growing of the cycle times,while the crack tip was affected by the compression stress,and the strain decreased with the increase of the cycle times.The stress intensity factor and the strain data at the sensor position along with the increase of the number of cycles in the process of crack propagation were gained;The relationship between crack length and cycle number,as well as strain and cycle number was obtained through Paris formula.Secondly,the fatigue crack propagation on-line monitoring platform was built,and the fatigue crack propagation process was experimentally studied.The strain data in the crack propagation process were collected by strain sensor,and the simulation results were verified.The curve of the crack length and the cycle times of each specimen,as well as the curve of the strain and the cycle times of each sensor were obtained.Finally,the curves got by simulation and experiment were fitted,and the fitting curves of the simulated and experimental strain and crack length were obtained.The model based inverse numerical algorithm was used to fuse the simulation and experimental data,and each model of the strain data was gained,and the pattern recognition of the crack length was realized.The remaining life of the structure was predicted through particle filter algorithm,and the prediction results were of high accuracy.