We need more than words and simple methods to describe the various different color patterns found on printed fabrics nowadays. The complexity in pattern identification has made the analysis and comparison difficult and will have to be managed manually. The automatic computer color separating system for printed fabrics, proposed in this paper, integrates a genetic algorithm (GA) and a self-organizing map network (SOMN) to automatically separate printed colors, so as to eliminate the time-consuming manual color segmentation and registration currently done in the industry. The system first uses a color scanner to record RGB color images of the printed fabrics and uses median filter processing to reduce color changes due to uneven light reflections arising from the fabric surface weaving texture. Then RGB color space is transformed to HSI color space so that color analysis can match human color sense and use customary procedures. Next, the GA is employed to search for color distributions that are the same as the original image of printed fabrics. The area of each sub-image is 9.06% of the original image, not only reducing color segmentation operation time, but completely reserving the print structure and color distribution of the original image. Afterwards, color characteristic values are obtained in HSI color space. Finally, SOMN is adopted for the color segmentation operation. According to our experimental results, this system can rapidly and automatically complete color separation and identify repeating patterns in images from printed fabrics.

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When measuring with an atomic force microscope (AFM), the probe would move to the prescribed position via the platform and its vibration would occur. To achieve precision positioning for the follow-up scanning and high-speed measurement, in this paper, mathematical modeling and control of the probe is focused to avoid the damage incurred by the collision between the probe and the sample and to obtain the high measurement in the scanning step. The Hamilton’s principle is firstly employed to derive the equation of motion and its boundary conditions. Next, the summation method, Lagrangian equation and Laplace transform are applied to obtain natural frequencies, a dynamic model of the AFM probe, and to work out the transfer function of the open-loop system. The proposed model is compared with two conventional AFM probe models: point–mass model and conventional cantilever beam model - where one of the ends of the cantilever and basic platform are assumed to be fixed. Finally, collocated control is exercised to designate the positions for actuator and sensor and the root locus method cooperated with proportional-integral-derivative controllers to simulate the performance of the control system. The result shows that the controller can ensure the stability of this continuous system and perform effective control.

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Printed fabrics are high value-added artifacts with rich colors and various patterns. Flawed products occur owing to uncertainties during the manufacturing process. Such defects waste not only raw materials and machine operating time, but also large amounts of labor to inspect, sift and sort. Hence, if the detection process for printed fabric defects could be automated, the product quality of printed fabrics could be increased, and industry efficiency could also be improved by reducing the requirement for manpower. So this study aims to develop such a defect detecting system to investigate printed fabrics with repeated patterns, locate flaw sites by the minimum repeated zone of repeated patterns, and finally find out the most common flaw type. The novelty of this technique is the introduction of an image processing technology known as the RGB accumulative average method (RGBAAM) to test and locate flawed zones, then use fuzzy logic to discern the flaw types. The RGBAAM has the merits of compactness and high execution speed, and it is an efficient algorithm for pattern recognition. The subject fabrics are printed fabrics with repeated patterns, and to interpret this kind of image, pure numeric calculations are faster than the widely used genetic algorithm. Experimental results show that this system can analyze and recognize 96.8% of defect types in printed fabrics, and therefore brings substantial benefits to control the product quality and improve current flaw detecting process in the printed fabric industry.

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