摘要
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 infrared thermography method for assessing the fatigue damage of metal components,Q235 steel specimens were selected,and thermography features of their surfaces were extracted by thermodynamic entropy and temperature two-dimensional（2D） information entropy.Through the collection of infrared thermography,the temperature and thermodynamic entropy accumulation of the fatigue damage course were deduced in combination with thermoelasticity,inelastic and thermal conduction effects.After constructing the surface temperature matrix,2D information entropy and its integral value of each damage stage were extracted,and the fatigue damage assessment model was established based on thermodynamic entropy and 2D information entropy method.Results show that both types of entropy can be used to evaluate the fatigue damage,however,the temperature information utilization and computational efficiency of 2D information entropy are higher,which can effectively avoid the numerical error of calculating plastic strain energy and facilitate obtaining more accurate assessment results.
出处
《兵器材料科学与工程》
CAS
CSCD
北大核心
2018年第1期23-29,共7页
Ordnance Material Science and Engineering
基金
国家自然科学基金(51605169)
安徽省自然科学基金(1508085QE91
1708085ME130)
过程装备与控制工程四川省高校重点实验室开放基金(GK201614)
关键词
疲劳损伤
红外热像
特征提取
熵分析
fatigue damage
infrared thermography
feature extraction
entropy analysis
