Are there always misjudgments in AOI inspections? Five Common problems and practical solutions
In today's industrial production, the precise inspection process is of vital importance, and AOI (Automatic Optical Inspection), as an advanced inspection technology, plays an indispensable role.
However, many enterprises encounter the problem of total misjudgment in AOI inspection in practical applications, which undoubtedly affects production efficiency and product quality. To this end, we have conducted an in-depth analysis of the five common problems in AOI inspection and provided practical and practical solutions to help enterprises enhance the accuracy and reliability of inspection.
Question 1: Frequent false alarms in character detection
Performance description:
The system determines components with qualified character printing/engraving and normal function as defective products, triggering false alarms.
Cause analysis:
The fundamental reason for the high misjudgment rate of AOI character detection lies in the instability of component character images and the singularity of detection standards
The character image is unstable
- Supplier differences: Different suppliers use different character printing/engraving techniques, ink/laser parameters, etc., which result in inconsistent color depth, thickness, contrast, etc. of the characters.
- Process fluctuation: Under different batches and production conditions from the same supplier, the quality of character printing/engraving may also fluctuate.
- Environmental interference: Environmental factors such as dust, stains, and reflections on the surface of components can also affect the clarity and recognition difficulty of character images.
The testing standard is single.
- Traditional AOI systems: They usually adopt rule-based traditional image processing algorithms, relying on pre-set character templates and fixed thresholds for comparison, and are difficult to adapt to the diversity and complexity of character images.
- Lack of adaptive ability: Unable to dynamically adjust recognition parameters based on different character features and image quality, resulting in a persistently high rate of misjudgment.
Solution:
In response to the above problems, OCR character recognition technology based on deep learning and adaptive light source technology can be adopted to enhance the recognition ability and adaptability of the AOI system for character images
Optimization algorithm - Deep Learning OCR algorithm
By adopting OCR character recognition algorithms based on deep learning, such as the advanced algorithms equipped in Shenzhou Vision AOI, it can learn from massive character image data, automatically extract character features, and recognize characters of different fonts, sizes, colors, and backgrounds, effectively improving the recognition accuracy.
Adaptive light source
According to the character printing/engraving processes of different components, it automatically adjusts parameters such as the light source Angle, brightness, and color to optimize the clarity and contrast of character images, providing high-quality image input for OCR recognition.
Question 2: Misjudgment caused by interference from light sources and the environment
Uneven lighting, frequent changes in ambient light, and unreasonable Settings of the device's sensitivity level can all lead to a decline in the quality of the collected images, thereby affecting the detection results of the AOI system and causing misjudgment.
Cause analysis:
Light source and environmental factors directly affect image quality. Unreasonable lighting conditions and equipment sensitivity Settings will cause the detection images to fail to truly reflect the status of components.
Solution:
Dynamically adjust the light source parameters
Fully consider the reflective characteristics of the material, set up multi-angle light sources, and through testing and optimization, find the most suitable combination of light angles to achieve the best image contrast and clarity. Meanwhile, calibrate the brightness of the light source regularly to ensure stable illumination.
Enclosed detection environment
Install a light shield in the detection area to block external light interference, creating an independent and stable environment for detection and ensuring the stability of image quality.
Question 3: The algorithm parameters are set too strictly or too loosely
Problem description:
During the AOI (Automatic Optical Inspection) process, if the threshold Settings in the algorithm model do not match the actual process standards, the following problems will occur
- Missed inspection: The threshold setting is too loose, resulting in some serious defects not being detected, posing quality risks.
- False alarm: The threshold is set too strictly, misjudging some minor defects or normal fluctuations as defective products, increasing the workload of manual re-evaluation and reducing production efficiency.
For example, take the detection of solder joint offset as an example. If the offset percentage threshold is set too strictly, some solder joints with slight offset but normal function may be judged as defective. Conversely, if the threshold is set too loosely, it may lead to the missed detection of some severely offset solder joints, affecting the reliability of the product.
Cause analysis:
The fundamental cause of the above problems lies in the rationality of the algorithm parameter Settings and the limitations of the algorithm itself
The parameter setting is unreasonable
- The threshold parameter setting in the algorithm model lacks scientific basis and has not been adjusted in combination with the actual process standards, resulting in the disconnection between the detection results and the actual production situation.
Limitations of the algorithm
- A single algorithm is difficult to meet the detection requirements of various components and various defect types, and it is also difficult to balance detection accuracy and efficiency.
Solution:
In response to the above problems, the strategy of phased debugging algorithm and the integration of multiple algorithms can be adopted to improve the detection accuracy and adaptability of the AOI system
Debug the algorithm in stages
- Initial stage: Appropriately lower the threshold, increase the defect detection rate, and avoid missed detections.
- Optimization stage: Gradually tighten the threshold, verify and optimize through a large amount of sample data, reduce false positives, and find the best balance point.
Adopt multiple algorithms
- Algorithm library: For instance, Shenzhou Vision AOI has adopted over 40 deep learning algorithms to build a rich algorithm library.
- Precise matching: For different types of components and different detection parts, the most suitable algorithm is selected for detection to improve the detection accuracy of complex defects.
Question 4: Misjudgment caused by differences in pad design and materials
Performance description:
When the pad size is not standard or there are differences in material packaging, the positioning components of the AOI system may be incorrect, leading to misjudgment and affecting the production progress and product quality.
Cause analysis:
The pad design does not meet the standards, and the material packaging is inconsistent, which causes deviations in the preset parameter positioning of the AOI system and makes it impossible to accurately identify the position and status of the components.
Solution:
Standardize pad design
During the soldering process design stage, ensure that the pad dimensions precisely match those of the component pins, avoid symmetrical arrangement of pads, reduce reflection interference, and enhance positioning accuracy.
Establish a material database
Record the character, color and other characteristic information of materials from different batches. During the detection process, the detection parameters are dynamically updated based on the material information to enable the system to adapt to the changes in the materials.
Question 5: Insufficient equipment maintenance and calibration deviations
Performance description:
After long-term use of the equipment, if the hardware ages (such as loose lenses, light source attenuation, etc.) and is not maintained in time, or if the origin sensor is not calibrated regularly during debugging, it will lead to a decrease in detection accuracy and cause misjudgment.
Cause analysis:
Equipment maintenance is the key to the normal operation of the AOI system. Hardware aging or failure to calibrate in a timely manner will affect equipment performance and detection accuracy, and may lead to misjudgment.
Solution:
Develop a maintenance plan
Conduct a comprehensive monthly inspection and maintenance of the equipment, including cleaning the lenses, checking the tension of the belts, calibrating the equipment coordinate system, etc., to ensure that all components are in the best condition.
Real-time monitoring of equipment status
With the help of professional software systems, key parameters such as light source brightness and camera resolution can be monitored in real time. Once the parameters are abnormal, a timely warning will be issued to facilitate technicians' timely maintenance and adjustment.
In conclusion, solving the problem of misjudgment in AOI detection requires approaches from multiple aspects. By comprehensively controlling image quality, detection programs, external interference, algorithm optimization, as well as equipment maintenance and calibration, enterprises can effectively reduce the misjudgment rate, enhance the accuracy and reliability of AOI detection, and provide more powerful quality assurance for industrial production.
It is hoped that the above five common problems and practical solutions can help everyone further improve the accuracy and reliability of AOI inspection and safeguard industrial production.
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