Visual inspection of soldered joints by using neural networks

Abstract

The problem of solder joint inspection is viewed as a two-step process of pattern recognition and classification. A modified intelligent histogram regrading technique is used which divides the histogram of the captured image into different modes. Each distinct mode is identified, and the corresponding range of grey levels is separated and regraded by using neural networks. The output pattern of the networks is presented to a second stage of neural networks in order to select and interpret a histogram's features. A learning mechanism is also used which uses a backpropagation algorithm to successfully identify and classify the defective solder joints. The proposed technique has the high speed and low computational complexity typical of nonspatial techniques.<>