The focus of this study is two-fold: first, to investigate the feasibility of thermal imaging for characterizing the development of chicken embryos; and second, to compare the effects of photo periods of 11 hours of light followed by 11 hours of darkness (11-11) versus 24 hours of darkness (24 dark) during the incubation cycle on embryo development. Previous reported work has used invasive methods, such as ultrasound, tomography, and MRI to study chicken embryos with some success. However, very little work has been reported on use of thermography, which is a non-invasive method. Results suggest that use of a cooling-heating-cooling cycle can reveal the anatomy of chicken embryos. A statistical comparison of image data from the two photo periods found no difference in the average cooling rates. However, the 11-11 group of eggs did hatch earlier overall than 24-dark group. Of the hatched eggs, all the chickens from the 24-dark group appeared to be in normal physical condition. However, two of the chickens from the 11-11 group appeared to have leg weakness shortly after hatching. Of these, one fully recovered the next day and the second remains the same after two days of observation. In addition, the second chicken took about 48 hours to fully emerge from its shell.

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This paper investigates an active thermography approach to probing hidden solder joint geometry. Ten boards were fabricated with the same number of solder joints and amount of solder paste (0.061 g), but using three solder joint geometries (60°, 90°, and 120°). The 90° angle solder pin represented a normal joint, and the 60° and 120° angle pins represented abnormal solder joints. Each board was covered with another board that had three openings just big enough to allow the pin terminals to protrude. A semi-automated system was built to heat and then transfer each board set to a chamber where an infrared camera was used to scan the board as it was cooling down. Each board set underwent the heating, cooling, and scanning process for five trials. Two-thirds of the data set was used for model development and one-third for model evaluation. An artificial neural network (ANN) was constructed to predict abnormal joints given thermal data. Results suggest that solder joints with more surface area cool much faster than those with less surface area. A Finite Element Analysis (FEA) of the heating up and cooling down process consistently predicted solder geometry using the ANN with 86% accuracy. This approach can be used not only to inspect bad solder joints (i.e., low reliability) but also to mass screen for cold solder joints during BGA assembly, since the air gaps in cold solder joints may cause them to cool more slowly than normal joints.