SMT circuit board assembly for industrial vision control systems

SMT circuit board assembly for industrial vision control systems

Image recognition machine for automatic inspection, workpiece processing and assembly automation machining, and control and monitoring of production processes. The image recognition process of the industrial vision system extracts relevant information from the original image data according to the task, and describes the image content in a highly generalized manner in order to interpret and judge certain contents of the image. The industrial vision system can be seen as a simplified primary machine vision system for tasks. In almost all industrial production areas, machine vision is required to replace human vision, especially for vision tasks that require high speed, accuracy or reliability. Automated inspection with industrial vision systems can interface with computer-aided design and computer-aided manufacturing, and is a necessary means of implementing computer-integrated design and manufacturing centers (CIDMAC). And CIDMAC can significantly improve the efficiency and product quality of small batch processing.

Application

Industrial vision systems can be used for a wide variety of tasks. According to the usage, the following examples can be cited: target positioning, orientation and identification in CIDMAC; defect inspection (such as cracking of metal components); sorting (such as picking nuts from the husk); grading (such as calculating the rate of fatness and thinness of meat) Determination of the liquid level in the bottle or tank; online measurement of the size of food, cloth or machined parts; inspection of the correctness of components; inspection of food, cosmetics, pharmaceutical contamination; detection of chemical leakage; instrument calibration; tool wear detection and product packaging inspection. The industrial vision system can be roughly divided into three categories: automatic inspection, component processing and assembly, and production process control according to the characteristics and complexity of the measured objects and the specific tasks to be processed.