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Instant Noodles Disposable Fork Defect Detection Equipment With Industrial Lens

Anhui Keye Intelligent Technology Co., Ltd
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Instant Noodles Disposable Fork Defect Detection Equipment With Industrial Lens

Brand Name : KEYE

Model Number : KVIS-T(A-25)

Certification : No

Place of Origin : CN

MOQ : 1 SET

Price : Negotiable

Payment Terms : L/C, T/T

Supply Ability : 1 set per 4 weeks

Delivery Time : 4 to 6 weeks

Packaging Details : Fumigation-free wood

Name : Disposable Fork Defect Detection Equipment

Material : SS 304

Algorithm : Deep learning

Weight : 300-500kg

After sale : Remote service

Light source : LED

MOQ : 1 SET

Package : Wood packing

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Instant Noodles Disposable Fork Defect Detection Equipment With Industrial Lens

Detection principle

The appearance defect detection equipment of disposable fork is designed for the comprehensive detection of the defects of disposable fork in the production process. However, in the production process, due to some disadvantages of automatic production, the possible problems in the production process of forks cannot be found in time. Manual inspection will cost a lot of time, manpower and material resources, so many manufacturers need to equip professional equipment to carry out comprehensive inspection of product.

Disposable forks are stably transmitted to a specific detection station.Then transmitted the shooting high-pixel sample image to image processing software by industrial camera, detecting and determining a good product and defective product via AI manually intelligent algorithm, and controlling the corresponding pneumatic components to remove the defective product, and finally get the qualified product. The detecting machine rear end conveying platform can interface the automated packaging mechanism.

Instant Noodles Disposable Fork Defect Detection Equipment With Industrial Lens

Equipment overview

-Online high speed detection, improve detection efficiency and accuracy, reduce human cost.

- High-pixel industrial camera accurate identification, the number of cameras is designed, accurate 0.1mm, speed 300 / min (can be customized according to the detection requirements).

- Compatible with multiple specifications, cost savings.

-Perform online detection and exclude for disabled fork defects:black spots, color difference, impurities, gaps, residues of material, overlaps, bubbles, air cavities, deformation, thickness, sizes, etc.

Name Disposable fork defect detection equipment Model Kvis-Sc02
Software Algorithm Deep Learning Voltage Ac220/50Hz
Voltage 200w Air Pressure Mpa
Transfer Method Lossless Delivery Detection Status Online Real-Time Detection
Detection Method High-Speed Industrial Camera Number Of Camera 1

Instant Noodles Disposable Fork Defect Detection Equipment With Industrial Lens

Our Advantages

  1. AI algorithm: high stability, adapting to the environment and background disturbance; different defect samples can be automatically identified after training.
  2. Dataization: Independent database, save multiple samples, analyze non-good products, and retain history.
  3. Multi-orientation: 360 ° in the inside and outside of the disposable fork, 360 ° in the outside without dead angle detection.
  4. Visualization: Real-time display sample image, operating state, real-time statistical test results and defects classification information.
  5. High precision: detection accuracy can reach 99.5%.
  6. Modularization, can flexibly increase or decrease the detection function according to customer actual needs.
  7. Easy to operate: It is easy to operate and easy to maintain.
  8. Safety: Medical grade material manufacturing, fully compliant with medical supplies production environment.

Instant Noodles Disposable Fork Defect Detection Equipment With Industrial Lens


Product Tags:

Disposable Fork Quality Inspection Machine

      

Disposable Fork Defect Detection Equipment

      
Quality Instant Noodles Disposable Fork Defect Detection Equipment With Industrial Lens wholesale

Instant Noodles Disposable Fork Defect Detection Equipment With Industrial Lens Images

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