Computer Vision Quality Control on a Conveyor Belt
Detect defects in production
Computer vision technology is increasingly being used for quality control on conveyor belts in manufacturing and production facilities. Here’s how it works:
Image Capture: A camera captures images of products moving on a conveyor belt. The camera can be fixed or mounted on a moving platform to capture images from different angles.
Image Processing: The images captured by the camera are processed using computer vision algorithms. These algorithms can identify defects and anomalies in the products, such as cracks, chips, or missing parts.
Classification: The products are classified into different categories based on their quality and defects. Products that meet the required quality standards are allowed to continue on the conveyor belt, while those that fail are removed from the line.
Sorting: Defective products can be sorted into different categories based on the type of defect. This allows manufacturers to identify the root cause of the problem and take corrective actions.
Reporting: Data on product quality and defects can be collected and analyzed to improve manufacturing processes and reduce defects in the future.
Computer vision on a conveyor belt detects quality anomalies such as color, cracks and the orientation of the moving object.
This application also measures the length and width of the object in millimeters, allowing for punctual quality control of bolts on a conveyor belt.
Using artificial intelligence for parts inspection avoids monotonous tasks on the part of operators.
In the quality control on the conveyor belt, the part area is used to determine if a defect exists, but AI can be applied in more advanced models to inspect various types of defects. When combined with high-performance hardware it is possible to achieve impressive inference speeds that allow analysis in real-time.
Applications
Computer vision has several applications in quality control on a conveyor belt in manufacturing and production facilities. Here are some specific examples:
Inspection of Product Quality: Computer vision algorithms can analyze product images captured on a conveyor belt to identify defects such as cracks, chips, discoloration, or missing parts. This can help ensure that only high-quality products are released into the market.
Label Verification: Computer vision can also be used to verify labels and packaging information such as barcodes, expiration dates, and product codes. This can help ensure that the right products are packaged and delivered to customers.
Counting and Sorting: Computer vision can be used to count products on a conveyor belt and sort them according to specific criteria such as shape, size, or color. This can help manufacturers streamline their operations and optimize their supply chain.
Machine Vision Inspection: Computer vision can be used to inspect the performance of machines on a conveyor belt. The algorithms can identify defects such as wear and tear, improper assembly, or malfunctioning parts. This can help prevent machine breakdowns and improve production efficiency.
Quality Control Analytics: Computer vision can be used to collect data on product quality and defects, which can be analyzed to identify trends and patterns. This can help manufacturers optimize their production processes and improve product quality.
Sending of product quality to the PLC/Robot
The vision system is able to measure the quality of products in order to identify non-conformities according to different sets of features.
Big Data
The system permits you to analyse the trend of the line during large periods of time.
Industry 4.0
Compliance with the requirements of Industry 4.0
Customization
The vision system could be customized according to the pick and place robotics.