Mixed-color illumination affects the quality of images in industrial vision system and it is important to optimize color and intensity for image acquisition. This study used simplex search to find the optimal illumination in a short amount of time. A typical color mixer synthesized various color of lights by changing the inputs of RGB power LEDs and passing the lights through an optical system. The image quality under mixed-color illumination was calculated according to the sharpness. For the purpose of optimal illumination using simplex search, a probe network was organized with N þ 1probing points for N inputs. The shape of the probe network, simplex, was varied through procedures of extension, contraction, and shrinkage. The inputs of the color mixer were changed until the size of the simplex became smaller than a threshold. The simplex search was tested for commercial semiconductor patterns, and was useful for finding the optimal illumination.

A tuning method was proposed for automatic lighting (auto-lighting) algorithms derived from the steepest descent and conjugate gradient methods. The auto-lighting algorithms maximize the image quality of industrial machine vision by adjusting multiple-color light emitting diodes (LEDs)-usually called color mixers. Searching for the driving condition for achieving maximum sharpness influences image quality. In most inspection systems, a single-color light source is used, and an equal step search (ESS) is employed to determine the maximum image quality. However, in the case of multiple color LEDs, the number of iterations becomes large, which is time-consuming. Hence, the steepest descent (STD) and conjugate gradient methods (CJG) were applied to reduce the searching time for achieving maximum image quality. The relationship between lighting and image quality is multi-dimensional, non-linear, and difficult to describe using mathematical equations. Hence, the Taguchi method is actually the only method that can determine the parameters of auto-lighting algorithms. The algorithm parameters were determined using orthogonal arrays, and the candidate parameters were selected by increasing the sharpness and decreasing the iterations of the algorithm, which were dependent on the searching time. The contribution of parameters was investigated using ANOVA. After conducting retests using the selected parameters, the image quality was almost the same as that in the best-case parameters with a smaller number of iterations.

This study proposed how to find optimal illumination for industrial vision in short time using random search algorithm and multiple color light sources. The fineness of an image captured by a monochrome camera is varied by illumination and can be evaluated by image sharpness. The relation between the sharpness and the illumination is nonlinear, so direct optimum methods are applicable to mix the multiple sources. Random search is one of the direct optimum methods and were derived from the sharpness as input and N driving voltages for N light sources. The random search was tested in an RGB mixer and reduced the number of iteration for optimal illumination compared with conventional equal step search.

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