As a common type of packaging machinery, bag-making machines can produce various forms of packaging bags. They mainly utilize the thermoplastic principle of plastic, through processes such as traction and feeding, heat sealing, and cutting, to turn pre-printed film into packaging bags. They are widely used in various production fields such as food packaging and light industry. The coordination level of various process links and parameters during the bag-making process directly affects the quality of bag-making. After years of development, bag-making machines have made significant progress in processes such as constant tension control during feeding, constant temperature control of the hot knife, and material movement control. Most of the process issues affecting the quality of bag-making depend on the mechanical structure and control system. However, issues like positioning cutting and defect detection cannot be simply solved by optimizing the mechanical structure and control system.
With the development of computer applications and image technology, machine vision technology, due to its ability to quickly obtain large amounts of information and easy automatic processing, has gradually been applied to packaging production processes. Jill Group has designed an image segmentation method based on maximum entropy threshold and utilized an adaptive Gaussian-guided image filtering algorithm to design an image denoising algorithm, applying machine vision to automatic detection of drugs in the pharmaceutical packaging production line. Machine vision technology is used to detect defects in the outer packaging of cigarettes on the production line, effectively identifying defects such as wire teeth and no wire on the packaging. Currently, machine vision applications in the field of bag-making machines in China are relatively limited. When producing corner-sealing bags, due to printing errors in the film material and errors in the color mark sensor for alignment, the cutting knife cannot accurately cut at the center of the corner, resulting in obvious burrs at the corners of the produced bags. Currently, bag-making machines typically use a double-cut mode, where the cutting knife operates twice continuously during cutting to avoid burrs. However, this method cuts off about 2 mm of waste material, which not only wastes material but also increases the wear of the cutting knife and the moving knife mechanism. Additionally, waste material may be mixed into the finished bags, leading to product quality issues. Machine vision systems, due to their high efficiency, strong anti-interference ability, and high positioning accuracy, can be used for positioning cutting of corner-sealing bags. Jill Group has established a vision platform and achieved precise positioning of the center of the corners of corner-sealing bags through soft triggering and stop-feed and snap mode. Based on this, a new corner single-cut control system was designed for the three-sided sealing stand-up zipper bag making machine using machine vision. The camera is mounted above the knife holder, and the PLC triggers the camera through the I/O port by hard triggering, which greatly improves the time accuracy compared to soft triggering. The camera uses a flying shooting mode to take pictures and process images during material feeding, achieving precise cutting of the current bag in the current bag-making cycle.
