The exhaustive quality control is becoming very important in the world́s globalised market. One example where quality control becomes critical is the percussion cap mass production, an element assembled in firearm ammunition. These elements must achieve a minimum tolerance deviation in their fabrication. This study outlines a machine vision system development using a three-dimensional 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 for scanning the pieces, and mechanical errors and irregularities in percussion cap placement. Owing to these problems, it is impossible to solve the problem using 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.
Inspec keywords: automatic optical inspection; computer vision; quality control; weapons; mass production; military equipment; cameras; learning (artificial intelligence); control engineering computing
Other keywords: image processing methods; percussion caps production; globalised market; three-dimensional machine vision; mechanical errors; quality control; firearm ammunition; percussion cap placement; machine-learning algorithms; inspection; percussion cap mass production; mechanical irregularity; high-speed movement; machine vision system development; minimum tolerance deviation; three-dimensional camera
Subjects: Optical, image and video signal processing; Manufacturing systems; Image sensors; Industrial applications of IT; Control engineering computing; Military circuits, components, and equipment; Defence industry; Inspection and quality control; Inspection and quality control; Production engineering computing; Computer vision and image processing techniques; Control technology and theory; Knowledge engineering techniques; Control applications in other manufacturing processes; Military control systems
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