The present work describes a new method to measure the contour position of plane reinforcement fabrics for the
manufacturing of structural composite parts. The pursued approach uses optical metrology based on laser light-section
technology. In detail, a laser line is projected over the edge of a fabric layer and acquired with a digital camera, which is
located under an offset angle to the laser sensor. This leads to a distinctive displacement of the laser line in the acquired
image, which is proportional to the distance between the sensor and the fabric layer. The distorted line can be described
as a step profile, to which an analytical function is fitted to calculate the horizontal edge position with sub-pixel
accuracy. To measure the whole layer position, the edges are scanned with the laser sensor to provide multiple contour
points. This allows the interpolation of the object contour. The interpolated contour can be compared with the specified
position and dimension of the textile layer. This enables a closed-loop control of the cutting and build-up process of the
preform. Thus, an efficient production process of fibre-reinforced plastics through an automated inline measurement is
possible.
This paper presents a segmentation algorithm for image processing, which is able to distinguish the background's area from the object's area based only on their texture characteristics. The main goal is to develop a segmentation algorithm which does not need any prior information about the object (form, colours, textures, brightness etc.) and which is also robust against industrial conditions (such as shadow effects and electromagnetic noise). The developed solution is based on a predefined and structured background (with a specific frequency in the grey values). The background frequency is compared with the object's texture frequency using a Fourier analysis together with a classification algorithm. Two different types of classification algorithms have been applied: statistical covariance analysis and Neural Networks. The algorithm has been evaluated using real textile images under distinct conditions showing a very appropriated result with a segmentation error smaller than 0,5 percent in the average. The paper finishes with a set of conclusions and perspectives for future works.
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