Abstract
Design of a glue dispenser route inspection system based upon the track of adhesive glue is the focus of this article. The defects of the glue track such as deformation, offset, scrape, and broken glue may affect the quality of production and efficiency. An automatic dispenser route inspection system in combination with the techniques of back-propagation neural (BPN) network with computer vision is developed. Before dispensing, the positioning process of the dispenser system is significant. A simple positioning method is developed to ensure the glued object mounted on a platform is in an acceptable position for glue shooting so that any likely failure due to inaccurate positioning is avoided. Thus, the positioning problem will therefore not influence the cause-and-effect failure investigation of other factors. The images of the track are acquired and then preprocessed to extract the features (coordinates of edge) for inspections. By checking the number of the searched pixels of the boundary of the glue track compared to the edge number of a uniform one, serious failure can be identified. For further diagnosis, six parameters including the average width and its standard deviation (SD) of the track, average offset and its SD, and the average deviation between the neighboring points on the left and right sides are designed as the input units in the input layer of a three-layer neural network and trained with experimental patterns. Using this BPN network system, the recognition rate is able to achieve 96.45% for additional arbitrarily chosen samples.
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Ting, Y., Chen, CH., Feng, HY. et al. Glue dispenser route inspection by using computer vision and neural network. Int J Adv Manuf Technol 39, 905–918 (2008). https://doi.org/10.1007/s00170-007-1285-4
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DOI: https://doi.org/10.1007/s00170-007-1285-4