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为提高使用红外热成像方法对金属构件疲劳损伤进行评估的效率和精度,选取Q235板试样,利用热力学熵和温度二维信息熵提取金属表面热像特征。通过红外热像采集,并结合热弹性、非弹性和热传导效应,对损伤过程的温度及热力学熵累积进行推演。构造表面温度矩阵,提取各损伤阶段的二维信息熵及其积分值,建立基于热力学熵和二维信息熵的疲劳损伤评估模型。结果表明,两类熵均可用于疲劳损伤评估,但二维信息熵的温度信息利用率和计算效率更高,且可有效避免塑性应变能计算的数值误差,有利于获取更精确的评估结果。
In order to improve the efficiency and accuracy of evaluating the fatigue damage of metal components by infrared thermography, the Q235 plate sample was selected and the thermography entropy and temperature two-dimensional information entropy were used to extract the thermal imaging features of the metal surface. The temperature and thermodynamic entropy accumulation of the damage process are deduced by infrared thermal image acquisition combined with the thermoelastic, inelastic and heat conduction effects. The surface temperature matrix was constructed, the two-dimensional information entropy and its integral value of each damage stage were extracted, and a fatigue damage assessment model based on thermodynamic entropy and two-dimensional information entropy was established. The results show that both types of entropy can be used for fatigue damage assessment, but the two-dimensional information entropy has higher temperature information utilization efficiency and computational efficiency, and can effectively avoid the numerical error of plastic strain energy calculation and help to obtain more accurate assessment results.