The restraint intensity （RI） of the pipeline girth welding joint was investigated using finite element method and experimental method to predict the cold cracking susceptibility of pipeline steel. The distribution o...The restraint intensity （RI） of the pipeline girth welding joint was investigated using finite element method and experimental method to predict the cold cracking susceptibility of pipeline steel. The distribution of RI along the girth weld was investigated to study the influence of welding position on the RL Subsequently, the effects of outer diameter （OD） and wall thickness of pipeline on the RI were studied with ABAQUS software. The results show that the RI is almost independent of welding position. The RI increased with the increasing wall thickness but decreased with the increasing OD. A prediction model of Rl was developed based on the effects of the OD and the wall thickness. It has been found that the predicted RIs were in good agreement with the experimental values. The maximum fractional error between the predicted RI and the experimental values was just about 10%. h was indicated that the errors were mainly caused by the heterogeneous of the weld bead.展开更多
Feature extraction and selection from signals is a key issue for metal magnetic memory testing technique. In order to realize the classification of metal magnetic memory signals of welding defects, four fractal analys...Feature extraction and selection from signals is a key issue for metal magnetic memory testing technique. In order to realize the classification of metal magnetic memory signals of welding defects, four fractal analysis methods, such as box- counting, detrended fluctuation, minimal cover and rescaled-range analysis, were used to extract the feature signal after the original metal magnet memory signal was de-noising and differential processing, then the Karhunen-Lo^e transformation was adopted as classification tool to identify the defect signals. The result shows that this study can provide an efficient classification method for metal magnetic memory signal of welding defects.展开更多
基金financially supported by the Tianjin Natural Science Foundation(No.11JCYBJC06000)the Key Project of Tianjin Municipal Science and Technology Support Program(No.11ZCGYSF00100)the Gansu Province Science and Technology Support Program(No.1204GKCA007)
文摘The restraint intensity （RI） of the pipeline girth welding joint was investigated using finite element method and experimental method to predict the cold cracking susceptibility of pipeline steel. The distribution of RI along the girth weld was investigated to study the influence of welding position on the RL Subsequently, the effects of outer diameter （OD） and wall thickness of pipeline on the RI were studied with ABAQUS software. The results show that the RI is almost independent of welding position. The RI increased with the increasing wall thickness but decreased with the increasing OD. A prediction model of Rl was developed based on the effects of the OD and the wall thickness. It has been found that the predicted RIs were in good agreement with the experimental values. The maximum fractional error between the predicted RI and the experimental values was just about 10%. h was indicated that the errors were mainly caused by the heterogeneous of the weld bead.
基金This work was supported by Tianjin Natural Science Foundation （No. 11JCYBJC06000） and Specialized Research Fund for the Doctoral Program of Higher Education of China （No. 20100032120019）.
文摘Feature extraction and selection from signals is a key issue for metal magnetic memory testing technique. In order to realize the classification of metal magnetic memory signals of welding defects, four fractal analysis methods, such as box- counting, detrended fluctuation, minimal cover and rescaled-range analysis, were used to extract the feature signal after the original metal magnet memory signal was de-noising and differential processing, then the Karhunen-Lo^e transformation was adopted as classification tool to identify the defect signals. The result shows that this study can provide an efficient classification method for metal magnetic memory signal of welding defects.
