Conventional video extensometers, using a measurement mark on specimen to obtain material strain, have a problem with deformation of the measurement mark. Therefore, the accurate position of the measurement mark is difficult to evaluate, and measurement accuracy is limited. To solve this problem, a strain measurement method based on a laser mark automatically tracking a line mark on the specimen is proposed. This method is using an undeformed laser mark to replace the line mark to calculate the specimen strain and eliminates the measurement error induced by the deformation of specimen marks. The positions of the laser mark and the line mark are achieved by using digital image processing. Automatic tracking is realized by means of an intelligent motor control. Also, the strain of the specimen is obtained by analyzing the movement trace of the laser mark. A video extensometer experimental setup based on the proposed method is constructed. Two experiments were carried out. The first experiment verified the validity and the repeatability of the method via tensile testing of the specimens of low-carbon steel and cast iron. The second one demonstrated the high measurement accuracy of the method by comparing with a clip-on extensometer.
A laser automatic tracking extensometer for material deformation measurement based on CCD is proposed. The image
processing methods of the laser mark localization and the automatic tracking of the mark line on the specimen are
studied for the extensometer. First, geometrical mean filter (GMF), harmonic mean filter (HMF) and inverse harmonic
mean filter (IHMF) using for the image processing are compared in order to select a suitable mean filter for removing
noises from the specimen images, and then the GMF is adopted for the de-noising of the images. Second, Sobel operator
is introduced to detect the edges of the specimen images. At last, the specimen images are reduced to eliminate unwanted
background information by pruning. Hough transformation of pretreated specimen images is also studied and linking
images algorithm is proposed based on the image gray distribution and the connectivity principle. The laser mark
localization and the automatic tracking of the mark line on the specimen are then implemented. The experimental results
show that the linking image algorithm is prior to Hough transformation on both recognition effect and recognition
efficiency.
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