At present, most glove production lines use manual inspection and finished product sampling to judge and screen out defective gloves. Facing a production line with a single-side production speed of 150-200 pieces/minute, 5-7 gloves need to be inspected per second. First of all, the inspection speed is far behind the production speed of the production line; secondly, due to human subjectivity, each It is difficult to unify individual testing standards, and the visual fatigue of human eyes after staring at gloves for a long time will also lead to a large number of missed gloves; in the end, manual inspection often can only see one side of the gloves, and it is impossible to conduct a comprehensive inspection of the gloves.

Using AI artificial intelligence deep learning algorithm, it can detect damage, oil stains and dirty spots on gloves during sports, automatically identify defects and accurately remove them. It can accurately detect defective gloves such as damage, oil stains and dirty spots, and can automatically remove defective gloves. False detection rate ≤0.5% (5 per thousand, no more than 5 false detections in 1,000 gloves)

According to the production status and needs of the enameled wire industry, combined with our company's many years of testing experience in the cable industry, we use diversified module combinations to quickly match needs, replace traditional manual operations with intelligent equipment, and help customers achieve intelligent manufacturing, improve quality and efficiency, Various spots, color differences, bubbles, concave and convex foreign objects, etc., mark the length, width and height of the defects.

In the field of industrial manufacturing and maintenance, it often happens that faults occur and cannot be eliminated in time because engineers cannot rush to the scene at the first time. However, smart glasses come in perfectly. Operators at the fault site only need to wear AR smart glasses and scan 3D images from the first perspective and upload them to the cloud. Expert engineers at a distance can accurately grasp the situation at the scene. Analyze the cause of the fault and guide the remote operator to perform on-site maintenance work.

Optical Automatic Detection Robot for Appearance Defects of Wind Roller是专业检测中大型滚子外圆、两端、凹穴和倒角的外观缺陷的检测设备。使用Robot control滚子移动，成功避免了大滚子的二次损伤。速度达到2000件每小时，可在各种环境下进行7*24小时不间断工作。先进的检测技术使检测功能更齐全，友好的人机界面使人工操作更便捷，

Optical Automatic Detection Robot for Appearance Defects of XL Wind Roller是专业检测中大型滚子外圆、两端、凹穴和倒角的外观缺陷的检测设备。使用Robot control滚子移动，成功避免了大滚子的二次损伤。速度最快30秒一件，可在各种环境下进行7*24小时不间断工作。先进的检测技术使检测功能更齐全，友好的人机界面使人工操作更便捷，

In fruit and vegetable production operations, the picking and harvesting process accounts for about 40% of the entire operation. The traditional manual picking method is basically a labor-intensive operation with high labor intensity. It is affected by weather and sunCaton&Hera time restrictions. It not only has low labor efficiency, but also affects the quality of work. There is no guarantee, and the safety hazards during the picking process cannot be ignored. The emergence of unmanned picking robots solves the current difficulties faced by fruit picking, realizes farmland harvest automation, can adapt to environmental changes, ensures work efficiency, and is in line with the development of the fruit industry. need.

The 3D manipulator is an automated device that can imitate human arm movements and has high flexibility and adaptability. This solution aims to design a 3D manipulator with high precision, fast response and strong load capacity to meet the needs of complex industrial application scenarios. The manipulator will achieve excellent performance through optimized mechanical structure design, electrical control system design and software algorithm design.