Production Operation AI Monitoring System
The production operation AI monitoring system (production operation error prevention AI monitoring system) can collect the operation process, the movement trajectory of employees and equipment, identify errors such as missing, wrong, missing, and missing in production operations, including employees' picking actions, movement trajectories, insertion positions, etc., and issue timely alarms. This not only improves production efficiency, but also reduces product quality problems caused by human errors.
In addition to real-time monitoring, the system can also monitor the compliance of the production process, retain and audit production related records and data, which is of great significance for the compliance management and risk control of enterprises.
Production Operation AI Monitoring System
The production operation AI monitoring system (production operation error prevention AI monitoring system) can collect the operation process, the movement trajectory of employees and equipment, identify errors such as missing, wrong, missing, and missing in production operations, including employees' picking actions, movement trajectories, insertion positions, etc., and issue timely alarms. This not only improves production efficiency, but also reduces product quality problems caused by human errors.
In addition to real-time monitoring, the system can also monitor the compliance of the production process, retain and audit production related records and data, which is of great significance for the compliance management and risk control of enterprises.
Through AI deep learning of video data, the system can identify various potential abnormal situations and issue timely warnings to avoid possible production accidents. At the same time, the real-time monitoring function of the system also helps to improve workers' work enthusiasm and sense of responsibility, as they know that their operational behavior is always under monitoring. It can not only improve production efficiency and product quality, but also ensure the safety of workers and create greater economic benefits for enterprises.
The error prevention recognition technology for the operation behavior of production line workers is widely applicable to various complex manufacturing industry scenarios.
At present, the behavior recognition system solution has been successfully applied in the production scenarios of more than 100 home appliances and kitchen appliance enterprises.
Application scenarios:
1. Real time monitoring of whether there are missing, incorrect, or duplicated accessories during product packaging operations;
2. Real time monitoring of product assembly operations to determine if there are any omissions in handling or locking screws.
3. Real time monitoring of product testing operations, whether the testing personnel follow the operating procedures for product testing, such as the use of testing tools and the execution of testing steps. Identify whether there are missing detections, misjudgments, and other situations to ensure product quality.
4. Monitor the safety status of the production site, promptly address safety hazards, and ensure the personal safety of employees. Real time monitoring of employees' personal protective equipment wearing, such as safety helmets, protective goggles, etc., to ensure that employees receive adequate protection during work.
At present, most of the existing traditional AOI detection equipment in the market does not have AI deep learning function, and generally has pain points such as high false alarm rate, which cannot achieve automated production and low production efficiency. The AI artificial intelligence system communicates with friendly AOI devices through the network, and based on the detection results of friendly AOI devices, uses a deep learning based AI artificial intelligence system for re evaluation to reduce false alarm rates and improve detection accuracy.
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The particle count re evaluation system is used to analyze the detection report data (particle count) of friendly particle indentation inspection machines. For products with particle counts less than the set threshold, traditional image algorithms and AI deep learning systems are used for secondary re evaluation, greatly improving the detection accuracy. The real NG data is uploaded through CIM and locked.
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Classify defects detected by friendly AOI equipment using AI for quality traceability. The classification accuracy reaches over 99%.
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The bonding foreign object detection system is used to detect foreign objects in the pictures of friendly particle indentation inspection machines. Due to the inability of particle indentation inspection machines to detect defects such as scratches, foreign objects, and fragments, it may miss detection. Our bonding foreign object detection system can detect the above defects and improve the yield rate of the production line.
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Production Operation AI Monitoring System The production operation AI monitoring system (production operation error prevention AI monitoring system) can collect the operation process, the movement trajectory of employees and equipment, identify errors such as missing, wrong, missing, and missing in production operations, including employees' picking actions, movement trajectories, insertion positions, etc., and issue timely alarms. This not only improves production efficiency, but also reduces product quality problems caused by human errors. In addition to real-time monitoring, the system can also monitor the compliance of the production process, retain and audit production related records and data, which is of great significance for the compliance management and risk control of enterprises.
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