材料科学与工艺
材料科學與工藝
재료과학여공예
MATERIAL SCIENCE AND TECHNOLOGY
2015年
2期
33-38
,共6页
邢海燕%葛桦%秦萍%刘长海%王犇%党永斌
邢海燕%葛樺%秦萍%劉長海%王犇%黨永斌
형해연%갈화%진평%류장해%왕분%당영빈
金属磁记忆%焊缝%缺陷等级%遗传算法%BP神经网络
金屬磁記憶%銲縫%缺陷等級%遺傳算法%BP神經網絡
금속자기억%한봉%결함등급%유전산법%BP신경망락
metal magnetic memory%weld%defect levels%genetic algorithm%BP neural network
针对金属磁记忆技术的焊缝缺陷等级定量化评定这一难题,通过对预制不同缺陷的Q345焊接试件进行疲劳试验,获得焊缝损伤演化临界状态的磁记忆信号特征规律。首次对照X射线定量检测标准和磁记忆检测结果,将焊缝损伤演化状态分为4个等级,即正常状态、应力集中、隐性损伤和宏观损伤。首次引入遗传算法优化的BP神经网络模型对焊缝等级进行磁记忆定量化评价。研究表明,遗传优化的BP网络模型与未优化的BP网络相比,预测结果更加稳定、误差更小,为工程实际中焊缝缺陷等级评定提供新的方法和依据。
針對金屬磁記憶技術的銲縫缺陷等級定量化評定這一難題,通過對預製不同缺陷的Q345銲接試件進行疲勞試驗,穫得銲縫損傷縯化臨界狀態的磁記憶信號特徵規律。首次對照X射線定量檢測標準和磁記憶檢測結果,將銲縫損傷縯化狀態分為4箇等級,即正常狀態、應力集中、隱性損傷和宏觀損傷。首次引入遺傳算法優化的BP神經網絡模型對銲縫等級進行磁記憶定量化評價。研究錶明,遺傳優化的BP網絡模型與未優化的BP網絡相比,預測結果更加穩定、誤差更小,為工程實際中銲縫缺陷等級評定提供新的方法和依據。
침대금속자기억기술적한봉결함등급정양화평정저일난제,통과대예제불동결함적Q345한접시건진행피로시험,획득한봉손상연화림계상태적자기억신호특정규률。수차대조X사선정량검측표준화자기억검측결과,장한봉손상연화상태분위4개등급,즉정상상태、응력집중、은성손상화굉관손상。수차인입유전산법우화적BP신경망락모형대한봉등급진행자기억정양화평개。연구표명,유전우화적BP망락모형여미우화적BP망락상비,예측결과경가은정、오차경소,위공정실제중한봉결함등급평정제공신적방법화의거。
In order to quantify defect levels of welded joints by using the metal magnetic memory technology ( MMM) , fatigue experiments were operated to find the MMM feature law of critical damage. The experiment material is Steel Q345 that is prefabricated with incomplete penetration and slag. In the light of the X ray detection national standard and MMM testing signals, welded joints are divided into four levels:normal, stress concentration, hidden damage and macroscopic damage. BP Neural Network ( BPNN) optimized by genetic algorithm is firstly presented to quantify defect levels based on MMM parameters, which indicates that the optimized BPNN is more stable and accurate than BPNN without optimization. This research provides a new scientific tool for practical engineering.