We are pleased to offer the Glove Vision Online Inspection System, an advanced and highly reliable solution meticulously engineered to enhance operational efficiency and ensure thorough and precise product testing. This state-of-the-art system integrates innovative technology to provide a comprehensive inspection process, setting new standards in the industry for accuracy and reliability in glove quality assessment.
To enhance the accuracy and efficiency of glove inspection, Shine Technology has developed and successfully launched the Dual-Mode Nitrile Medical Glove Vision Inspection System.
This equipment employs advanced AI artificial intelligence software to perform comprehensive inspection of gloves in motion, including fingers, palms, and cuffs. It is capable of identifying defective products and removing them.
With the use of advanced Artificial Intelligence (AI), comprehensive detection of fingers, palms, and cuffs of gloves in motion can be achieved. Defective products are identified and removed efficiently.
During the movement of the gloves, the high-speed camera completes the shooting, and the collected images are uploaded to the host computer for image preprocessing and then sent to the deep learning model for inference. The results of the model inference are sent to the system to classify the gloves into qualified or defective categories. . The equipment does not take any measures for qualified gloves and releases them to the counting machine for packaging; for defective gloves, the control system automatically controls the rejecting machine and counting machine to remove the defective gloves.
We are delighted to present the Intelligent Sorting Machine, a sophisticated and exceptionally reliable solution meticulously designed to optimize operational efficiency and deliver precise, thorough product sorting. This cutting-edge machine incorporates advanced technology to offer a comprehensive sorting process, establishing new benchmarks in the industry for accuracy and dependability in sorting operations.
Artificial intelligence software is used to perform all-round inspection of parts and remove them. Through deep learning algorithms, defects are classified, located, alarms are output, and defect images are displayed. At the same time, high-precision cameras are used to measure the size of parts, and those that do not meet the size range will be automatically removed.
Detection defects include:
Color difference, color, leak process, mixing, characters, logo, corner drop, crushing, scratches, bumps, knife marks, cracks, etc.
Dimensional measurements include:
Length, width, height, thickness, circle, inner diameter, outer diameter, angle, RDegree, contour, parallelism, verticality, concentricity, roundness, rectangle, trapezoid, etc.
| Industry | Testable products (including but not limited to) |
|---|---|
| Semiconductor chip | Silicon wafer, wafer, packaging inspection |
| Magnetic materials | Appearance inspection of various magnetic materials such as columnar, block, tile, etc. |
| Bearings/seal | Bearing inner and outer rings, dust cover, sealing ring, cage |
| Electronic components | Mobile phone parts, computer parts, home appliance parts |
| Precision hardware | Fasteners, hardware, connectors, watch parts |
| Clothing accessories | Resin buckle, shell buckle, metal buckle |
| Medical | Medical equipment, medicines, medicine boxes |
| Agriculture/food processing | Fruit screening and grading, oil crop screening |
Our blister machine utilizes image vision inspection technology to accurately detect various appearance defects in tablets. By employing advanced AI software, it conducts comprehensive inspections of moving tablets, identifying and removing defective products. Through deep learning algorithms, the system classifies and locates defects, outputs alerts, and displays defect images to ensure quality.
Artificial intelligence software is used to perform all-round inspection of parts and remove them. Through deep learning algorithms, defects are classified, located, alarms are output, and defect images are displayed. At the same time, high-precision cameras are used to measure the size of parts, and those that do not meet the size range will be automatically removed.
Detection defects include:
1. Tablet defects: irregular blister, stains on front aluminum foil, two tablets in the same blister, missing tablets.
2. Capsule blister: aluminum foil burrs, damaged capsules, blurred batch number or date, foreign objects in the blister.
3. Blister reverse side: wrinkled back aluminum foil, damaged back aluminum foil, deformed aluminum foil, incomplete aluminum foil coverage.
| System Parameters | Data |
|---|---|
| Defect Detection | 23 Types |
| Inspection Accuracy | 0.05mm² |
| Inspection Speed | ≤400 boards/min |
| Defect Detection Rate | 99.90% |
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