Impurity Visual Quality Inspection Machine for Clear Cosmetic Bottles

Product backgound

Contamination and foreign objects such as air pockets, dust, particles or hair remain one of the leading causes of product recalls in the packaging industry. Ensuring consistent product quality with minimal operator intervention enhances and protects brand reputation while maximizing productivity and investment. Relying on people to perform quality checks can be costly, especially when looking for defects in a high-speed production environment. Operators are required to perform mundane and repetitive tasks, such as observing through a microscope on a workbench. Traditional machine vision development and optimization can be time-consuming for inspections that require operators, given product variance and possible defects.

KEYE deep learning locates, analyzes and classifies complex contamination issues in real-time, preventing contaminants from entering the supply chain of high-volume production lines. Deep learning combines the inspection capabilities of humans with the automation, scale, and repeatability of computer systems. Robotics can then be used to ensure that machine processing and vision tools work together to detect the most complex anomalies that even an operator might miss. The end result is fewer recalls, lower rework costs, and full product image capture and traceability.

Inspection principle

Obtain the image signal of the detected target through image acquisition, convert the image signal into a digital signal through A/D, and then transmit it to a special image processing system, perform operation and analysis according to the distribution information of brightness, pixel and color to obtain the target characteristics, and finally according to The judgment criterion set before outputs the result of the system operation, and the control system controls and drives the corresponding peripheral mechanism to perform certain processing.

The processes of image acquisition, image preprocessing and foreign object detection are respectively implemented to simulate the entire production line shown in the figure. Realize on-line detection of bottled liquid foreign matter, automatically reject unqualified products, and greatly improve the accuracy and speed of on-line detection in terms of detection methods.

Detection details&System equipment

The glass bottle foreign body detection method adopts the moving target detection method. The detection of foreign objects in the case of relatively regular motion is studied. Through the rapid and accurate acquisition of real-time image information of the liquid in the glass bottle, the detection, analysis and research of foreign objects in the bottled liquid are realized, and specific judgments are made to ensure the safety of the liquid quality.

If the posture of the moving target changes, the surrounding scene changes, etc., these feature changes need to be modeled and detected. The glass bottle itself is dirty and the air bubbles in the dropper are the key problems in the detection of the detection system, which directly affect the effectiveness and accuracy of the entire detection system. False detection will affect the system's judgment on the detection of foreign objects in the entire glass bottle.

Equipment parameters

After-sale service

The company has a complete technical service team and rapid response mechanism, and has dedicated service specialists for each customer, who can receive technical consultation and fault reports from customers at any time. And to ensure rapid response to customer emergencies, to ensure that customers receive satisfactory service.During the epidemic or due to special reasons, when after-sales engineers are unable to reach the site, the service center can remotely adjust customer equipment for troubleshooting and technical consultation.

After the equipment arrives at the customer site, the after-sales engineer arrives in time to carry out equipment installation, commissioning, and operation training. The product quality of the whole machine is traceable, and the quality warranty period is 1 year from the date of acceptance. In the event of non-human faults during the warranty period, after-sales engineers will quickly arrive at the site or provide remote guidance for free maintenance.

About Us

Development History

The technology center is composed of algorithm department, software department, optics department, hardware department and mechanical department. The algorithm team currently has 7 PhDs, ensuring that the AI algorithm has the ability to continuously update.

Organization Structure