The aim of this research is to construct an appropriate back-propagation neural network topology to automatically recognize neps and trash in a web by color image processing. After studying the ideal background color under moderate conditions of brightness and contrast to overcome the translucent problem of fibers in a web, specimens are reproduced in a color BMP image file format. Assuming that neps and trash can be distinguished without difficulty from the color image, the image-taking device in the system can be easily altered as long as the optical conditions for other color image resources (i.e., CCD) are considered to ensure image quality. With a back-propagation neural network, the RGB (red, green, and blue) values corresponding with the image pixels are used to perform the recognition, and three categories (i.e., normal web, nep, and trash) can be recognized. The numbers and areas of both neps and trash can also be determined. According to experi mental analysis, the recognition rate can reach 99.63% under circumstances in which the neural network topology is 3-3-3. Both contrast and brightness are set at 60% with an azure background color. The results show that both neps and trash can be recognized well, and the method is suitable not only for cotton and man-made fibers of different lengths, but also for different web thicknesses as to a limit of 32.9 g/m2. Since neps and trash in a web can be recognized, yarn quality not only can be assessed but also improved using a reference for adjusting manufacturing parameters.
Moisture is one of the major contributing factors in fracture and reliability issues for microelectronic packaging. To characterize the moisture-induced stress distribution inside the packaging structure, an in situ, quantitative, and nondestructive experimental methodology is needed. This paper proposes the use of piezoresistive sensors to measure moisture-induced stress in a plastic low profile, fine pitch, ball grid array (LFBGA) packaging. The measurements include hygroscopic swelling stress extractions and real-time stress monitoring of the popcorn phenomenon, and the results associated with gravimetric analyses are reported. Postreflow scanning acoustic microscope (SAM) inspection results and cross section observations are used as experimental verification. Comparing with thermal stresses previously measured on the same package, it is found that the hygroscopic mismatch stress is significant and important for package engineers. In addition, piezoresistive sensors were proven useful in this work for recording popcorn occurrence and monitoring the stress drops at the popcorn initiation.Index Terms-Electronic packaging, hygroscopic mismatch, moisture-induced effect, piezoresistive sensor, popcorn failure.
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