Thread Content
Currently, there are increasing demands for measurement accuracy, efficiency, and automation, and traditional inspection methods and technologies can no longer meet these requirements. Image-based dimension measurement is a modern measurement method based on optics, involving multiple disciplines such as automation, pattern recognition, and image processing. It not only boasts advantages such as high measurement accuracy, no damage to the actual sample, and a wide range of applications, but it can also overcome the shortcomings of traditional measurement methods, such as poor real-time performance and inconvenient operation, thus meeting the requirements of modern measurement. An online diameter measuring instrument is a testing device used in automated production lines, and its software-based control platform helps workers to more clearly and intuitively identify the problems existing in the production process. This article mainly introduces the fluctuation graph of measurement and control software. The fluctuation graph interface displays the variations in size fluctuations of the eight measurement values through fluctuation curves; it can be opened as a separate interface, and when the measurement data remain within the set range, the data is shown in green ； When the measurement data is out of range, the color of the data turns red. By analyzing the fluctuations in the data, it is possible to identify the quality trends of each batch of rolled products, determine which parts are more likely to exceed the specified limits, and make adjustments in advance. It is also possible to view the specific details of a particular out-of-tolerance condition. A fluctuation graph provides a more intuitive way to view the overall quality of the rolled products. The fluctuation of the outer diameter data is analyzed using a fluctuation diagram, by comparing it with the nominal value. An analysis is conducted on the factors that affect the quality of rolled products. The display of such fluctuation charts enables better monitoring of the variations in the data from various detection sources, allowing for the identification of abnormal values related to the outer diameter. This helps to identify the factors that impact quality, providing support for decision-making in automated production systems and thus enabling high-quality and efficient production.