MMM accurate location model of early hidden damage in welded joints based on PSO and MLE
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摘要: 針對由于焊接殘余應力、磁場噪聲等干擾,造成磁記憶檢測在焊縫早期隱性損傷位置定量評價上的困難,提出基于粒子群算法優化的最大似然估計磁記憶梯度定量模型.通過對預制未焊透缺陷的Q235焊接試件進行焊縫疲勞拉伸實驗,同步對比掃描電鏡和X射線檢測結果,發現磁記憶信號梯度對早期隱性損傷位置反應比較敏感,并獲得了梯度隨著與隱性損傷的距離增大而減小的衰減變化規律,構建隱性損傷位置參數與磁記憶梯度的非線性函數,考慮磁場噪聲對隱性損傷定位結果的影響,引入最大似然估計建立目標函數,進一步考慮目標函數的非線性容易陷入局部極值而非全局極值的問題,采用具有全局搜索能力的粒子群算法對目標函數進行優化,建立基于粒子群最大似然估計的焊縫隱性損傷位置磁記憶定量模型,驗證結果表明定位誤差僅為3.48%,為實際工程中利用磁記憶技術及時發現早期隱性損傷并精確定位提供了新的思路.Abstract: To accurately locate hidden damage in welded joints, a metal magnetic memory (MMM) gradient model was present based on maximum likelihood estimation (MLE) optimized by particle swarm optimization (PSO). Tabular welded Q235 specimens were subjected to fatigue tensile experiments. Using electron microscope scanning and X-ray detection, it is found that MMM gradient K is sensitive to the location of early hidden damage and decreases with an increase in distance from it. A nonlinear function is then presented between the position parameter and the MMM gradient. MLE is introduced to establish the nonlinear objective function. Furthermore, considering the nonlinear objective function is easy to get into the local rather than the global extremum, the PSO is adopted to optimize the objective function for a global search ability. The results show the location error of the model is 3.48%, therefore MMM provides a new tool for the identification and accurate location of early hidden damage in welded joints.
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參考文獻
[1] Dubov A A. Development of a metal magnetic memory method. Chem Petrol Eng, 2012, 47(11-12):837 [8] Xing H Y, Ge H, Dai G G, et al. Maximum likelihood estimation modeling of welded joints based onmetal magnetic memory parameters. Appl Mech Mater, 2017, 853:458 -
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