Others
More precise detection of wafer/chip defects
AI algorithms can accurately identify subtle nano-scale defects like scratches, stains, and indentations on wafer/chip surfaces, while traditional AOI systems have limited capabilities in detecting these minor defects.
Automated classification of defect types
AI not only can detect the presence of defects, but also automatically categorize them into different types like scratches or foreign particle adhesion based on their characteristics (shape, size, etc.), providing a basis for subsequent analysis and handling.
Stronger pattern defect detection capabilities
The AI vision system can accurately inspect circuit patterns on wafers/chips for issues like open circuits or short circuits, whereas traditional AOI has relatively weaker abilities in this regard.
Significantly improved detection efficiency
AI-based detection is far faster than traditional AOI, enabling high-volume, inline inspection without impacting production pace, thereby enhancing manufacturing efficiency.
Reduced human intervention and subjective errors
AI-based inspection has the advantages of automation and intelligence, minimizing the subjective biases introduced by manual operations, and greatly improving the consistency of inspection quality.
Enhancing defect detection for light guide plates and diffusion boards
Optoelectronic/display products have extremely high uniformity requirements for light guide plates and diffusion boards. Any minor scratches, stains, or deformations can cause uneven brightness. AI-based vision algorithms can detect these tiny defects with high precision, whereas traditional AOI has limited capabilities in this area.
Accurate detection of fine circuit pattern defects
The metal conductive layers and circuit patterns on panels are becoming increasingly miniaturized, making it difficult for traditional AOI to fully detect subtle defects like breaks and cracks. Leveraging powerful pattern recognition capabilities, AI vision systems can detect these fine circuit defects with high accuracy.
Improved detection rate for color filter and CF defects
Color filter defects come in diverse forms, requiring inspection for issues like bright spots, dark spots, and contamination. AI technology can simultaneously detect multiple defect types, achieving a far higher detection rate compared to traditional AOI focused on single defect types.
AOI utilizes high-speed, high-precision vision processing technology to automatically inspect various assembly errors and soldering defects on PCBs, ICs, and electronic components.
The scope of PCB, IC, and electronic components can range from fine-pitch, high-density boards to low-density, large-size boards.
Online inspection solutions are provided to improve production efficiency and soldering quality.
By using AOI as a tool to reduce defects, errors can be identified and eliminated early in the assembly process, achieving good process control.
Early detection of defects will prevent bad boards from moving to subsequent assembly stages.
AOI will reduce repair costs and avoid the scrapping of unrepairable PCBs, ICs, and electronic components.
Enhancing defect detection for complex metal/mechanical components
Mechanical and automotive parts often have complex shapes, and AI algorithms can better identify subtle defects such as cracks, scratches, and deformations on these intricate, high-precision components, improving the detection coverage and accuracy.
Handling diverse metal materials and surface characteristics
Different metals like steel and aluminum have varying reflective properties, which traditional AOI systems struggle to cover comprehensively. AI systems can self-adapt by learning from extensive sample data, flexibly handling various metal materials and surface conditions.
Improving defect detection for critical functional components
For core functional components in machinery and vehicles, such as engine blocks and brake discs, AI can more precisely detect minor defects that may impact safety and service life, enhancing quality control for these critical parts.
Shortening programming time for complex product inspections
With the wide variety of mechanical and automotive products, traditional AOI requires rewriting inspection programs for each new product. AI systems can quickly learn and adapt to new products, significantly reducing the programming and deployment time.
Enhancing high-speed production line inspection adaptability
Mechanical and automotive production lines often operate at very high speeds, making detection more challenging due to increased product motion. AI-based vision inspection algorithms can better handle high-speed scenarios, ensuring stable and reliable inspection.
Real-time problem identification and resolution
Using AOI fabric inspection or plastic molding machines can instantly detect defects, allowing factories to promptly resolve issues and reduce recalls and waste. Identifying defects before shipment helps improve customer satisfaction and increase the likelihood of repeat orders.
Reducing human errors
Before replacing manual inspection, factory workers had to rely on their eyes to visually identify defects for long periods. Human errors, fatigue, and inadequate lighting can all impact manual inspection, causing defective products to slip through unnoticed. AOI fabric or plastic molding machines can mitigate such risks.
Reducing customer complaints and increasing ROI
With a well-trained identification model, AOI can easily detect a variety of fabric defects. Not only is the inspection process more streamlined, but it also reduces human oversights. Factories can then take on large-scale orders, increasing their profit potential and investment returns.
Lowering production costs
Manual fabric inspection has a limit of 20-30 minutes of concentrated effort before fatigue sets in, and prolonged operation can be harmful to eye health. Automated inspection machines effectively solve issues like labor turnover, worker fatigue, and recruitment challenges that can cause production capacity fluctuations.
More precise detection of food surface defects
AI algorithms can better identify subtle scratches, cracks, and indentations on food surfaces, whereas traditional AOI systems have limited capabilities in detecting these minor defects.
Accurate identification of foreign objects within food
Leveraging auxiliary equipment like X-rays or infrared, AI can effectively inspect food products for the presence of foreign objects, impurities, or metal contaminants, improving control over the internal quality of food.
Automated food shape recognition and grading
AI can grade and sort food products based on parameters like shape and size, ensuring they meet the specified standards, which aids in product packaging and shipping.
Rapid inspection of food packaging integrity
AI can automatically check for issues like damage or poor sealing in food packaging, enabling immediate detection of any unexpected harm during transportation.
Improved detection of subtle surface defects on medical devices
AI algorithms can precisely identify nano-scale defects like scratches, burrs, and indentations on the surfaces of medical devices such as syringes and catheters. Traditional AOI systems have limited capabilities in detecting these minor defects.
Accurate inspection of medical packaging integrity
The AI vision system can automatically inspect medical product packaging for issues like damage and seal integrity, ensuring the completeness of the sterile barrier and avoiding contamination risks during transportation.
Realization of medical label character recognition
Some medical products feature complex labels with information like batch numbers and expiration dates. AI's powerful optical character recognition (OCR) capabilities can accurately identify and verify the correctness of these labeling data.
Improved inspection efficiency and reduced human risks
The AI inspection process is highly automated and fast, enabling high-volume inline detection. This reduces the safety risks and judgment biases associated with manual operations, improving the consistency of inspection quality.
Improved detection accuracy
AI can learn the features of small, complex defects through deep learning from large datasets, surpassing human visual inspection capabilities. However, it also requires capturing high-quality images and integrating multiple feature extraction techniques to detect the smallest defects.
Flexible defect definition and classification
AI can self-learn defect characteristics, significantly reducing the manual definition efforts. It can detect defect types missed by traditional rule-based approaches, and also perform accurate classification of defect categories (e.g. scratches, bubbles). But when the defect samples are imbalanced, strategies like data augmentation and class weighting adjustments are needed.
Highly adaptive optimization of inspection
AI can automatically optimize the inspection parameters and strategies based on different glass properties, improving robustness. For curved glass surfaces, AI can integrate techniques like 3D vision to adapt to these complex 3D scenarios.
Automated, unattended operation
AI-based inspection systems can run continuously in an automated manner, improving efficiency and reducing labor costs. However, it is important to handle complex background interference issues and enhance the model's resilience to such environmental noises.
Improved detection of small defects on printed products
AI algorithms can precisely identify subtle defects on labels/printed products, such as small stains, scratches, and color variations, while traditional AOI systems have limited capabilities in detecting these minor defects.
Accurate text/code recognition Labels/printed products often feature text, barcodes, and other encoded information.
AI has powerful optical character recognition (OCR) and barcode recognition capabilities, allowing it to accurately identify and verify the correctness of these encoded data.
Efficient inspection of complex patterns and image defects
Labels and packaging often have intricate graphic designs. The AI vision system can effectively detect issues like pattern segmentation, image quality, and missing elements, whereas traditional AOI has limited abilities in handling image-related defects.
Significantly improved inspection efficiency
The AI inspection process is highly automated and fast, enabling high-volume inline detection without disrupting the production line, greatly enhancing the overall inspection efficiency.
Others
Solutions：
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result(s)
The GS2000 can acquire an image of a single wooden board in 0.5 seconds. In image processing and analysis, it uses a neural network classifier and a Bolb module. The neural network is the core of defect detection and classification, and the Bolb can further highlight defects such as holes, cracks, breaks, stains, and gaps, effectively improving the processing efficiency and reliability of the neural network.
Software / Hardware
The "Digital Twin AI Inspection System" has a set of identical automated systems in both the real world and the metaverse, which operate in real time synchronization. The inspection system uses two vision cameras, one of which is paired with AI inspection software to perform defect detection. The other camera integrates a robotic arm and conveyor belt tracking system to accurately pick up moving PCBs and sort them into OK or NG areas based on the AI inspection results.
Software / Hardware