In this paper, some of the problems arising from the acquisition of digital photography intended to determine the dimensional accuracy of products are discussed. Different equipment (digital cameras, lenses, and lighting) was used when acquiring photos in different formats. In mechanical engineering, the tolerances of dimensional measurements of products range from a few microns to 100 microns. Based on that, the technical specifications and capabilities of the cameras and lenses used are analyzed. In addition to the sensor, the resolution of the photo is often limited by the lens. Prior to the acquisition of photographs, problems such as lighting, contrast, sharpness, and the problem of central projection are addressed. During acquisition, technical specifications of the lens such as focal length, minimum focusing distance, aperture, and other lens features that cause acquisition problems are considered. This research and analysis led to valuable insights and conclusions that will facilitate the preparation of the acquisition of digital photography to determine the dimensional accuracy of the product.
This paper discusses the possibilities of an automated solution for determining dimensionally accurate and defective products using a computer vision system. In a real industrial environment, research was conducted on a prototype of a quality control machine, i.e. a machine that, based on product images, evaluates whether the product is accurate or defective using computer vision. Various geometric features are extracted from the obtained images of products, on the basis of which a fuzzy inference system based on Fuzzy C-means clustering features is created. The extracted geometric features represent the input variables, and the output variable has two values - true and false. The root mean square error in the evaluation of the accuracy and defectiveness of products ranges between 0.07 and 0.16. Through this research, valuable findings and conclusions were reached for the future research, since this topic is poorly examined in the most renowned databases.
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