Voids are one of the major defects in ball grid array （BGA） solder joints due to a large amount of outgassing flux that gets entrapped during reflow. X-ray nondestructive machines are used to make voids visible ...Voids are one of the major defects in ball grid array （BGA） solder joints due to a large amount of outgassing flux that gets entrapped during reflow. X-ray nondestructive machines are used to make voids visible as lighter areas inside the solder joints in X-ray images for detection However, it has always been difficult to analyze this problem automatically because of some challenges such as noise, inconsistent lighting and void-like artifacts. This study realized accurate extraction and automatic a-nalysis of void defects in solder joints by adopting a technical proposal, in which Otsu algorithm was used to segment solder balls and void defects were extracted through opening and closing operations and top-hat transformation in mathematical mor-phology. Experimental results show that the technical proposal mentioned here has good robustness and can be applied in the detection of voids in BGA solder joints.展开更多
Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter wit...Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.展开更多
基金中央高校基本科研业务专项资金资助项目(HUST:2012QN036)材料成形与模具技术国家重点实验室自主创新基金资助项目(02-25-110093)+1 种基金国家科技支撑计划资助项目(2012BAF08B03)欧盟框架七项目"Casting of Large Ti Structures"资助项目(265697)
基金National Science and Technology Major Project of the Ministry of Science And Technology of China(No.2013YQ240803)Shanxi Programs for Science and Technology Development(Nos.20140321010-02,201603D121040-1)Scientific and Technological Innovation Programs of Higher Education Institutions of Shanxi Province(No.2013063)
文摘Voids are one of the major defects in ball grid array （BGA） solder joints due to a large amount of outgassing flux that gets entrapped during reflow. X-ray nondestructive machines are used to make voids visible as lighter areas inside the solder joints in X-ray images for detection However, it has always been difficult to analyze this problem automatically because of some challenges such as noise, inconsistent lighting and void-like artifacts. This study realized accurate extraction and automatic a-nalysis of void defects in solder joints by adopting a technical proposal, in which Otsu algorithm was used to segment solder balls and void defects were extracted through opening and closing operations and top-hat transformation in mathematical mor-phology. Experimental results show that the technical proposal mentioned here has good robustness and can be applied in the detection of voids in BGA solder joints.
基金Project(20090162120084)supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(08JJ4014)supported by the Natural Science Foundation of Hunan Province,China
文摘Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.
