Abstract
The exhaustive quality control is becoming very important in the world’s globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.
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Tellaeche, A., Arana, R., Ibarguren, A., Martínez-Otzeta, J.M. (2010). Automatic Quality Inspection of Percussion Cap Mass Production by Means of 3D Machine Vision and Machine Learning Techniques. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_33
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DOI: https://doi.org/10.1007/978-3-642-13769-3_33
Publisher Name: Springer, Berlin, Heidelberg
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