Apply the Classification and Segmentation technology of Solomon SolVision AI image platform to identify defect features. First, use the Classification tool to judge whether the wafer has too many defects and eliminate the defective products that cannot be repaired. Then, use image processing technology to segment the wafer image, and use the Segmentation tool to detect the defects in the image, record their features, coordinates, area and other information, which greatly improves the efficiency of subsequent repairs.

At present, most yarn factories still rely on manual inspection, which has a high missed detection rate and consumes a long time. There are many types of yarn defects, such as paper tube stains, deformation, dirty yarn, broken yarn, thrown yarn, fluff, and two-color yarn. Manual inspection is not conducive to actual quality requirements, and automatic optical inspection (AOI) is also difficult to detect when facing non-fixed defects, and the false detection rate is high, and manual re-inspection is still required. In order to allocate labor costs to more efficient work, yarn inspection should seek higher efficiency inspection solutions.Using SolVision's Segmentation technology, feature extraction is performed on various defects on paper tubes and yarns, and AI model training is performed to enable AI to learn to identify defect features and quickly and accurately find various defects. It can effectively improve the detection rate, finished product yield and reduce the quality inspection burden. With the increase in the number of learning samples, the ability of AI to identify defects can be continuously optimized, and the learning results can also be quickly introduced into various production lines.

1. Web-based architecture, allowing multiple users to log in remotely through the domain
2. Integrate and store a large amount of defect data and images detected by AOI equipment, which can be used for production history statistical analysis, real-time monitoring of online AOI equipment defect detection status, defect photo viewing, product defect Map overlay and defect type judgment Code and other functions
3. Can be combined with AI for big data analysis and feedback to production equipment to issue warnings for production anomalies

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.

A new generation of vision systems has been developed that makes machine vision the preferred inspection technology for wheel fastener inspection. The In-Sight 2000 vision sensor can learn the known good appearance of components and compare many different features to determine if they are pass or fail. The In-Sight 2000 is small, has a fully independent light and lens, and is rugged enough to withstand harsh factory environments.

Using Cognex Deep Learning, it is easy to analyze metal piston weld seams by yourself. Engineers can use the Cognex Deep Learning defect detection tool in supervised mode to train the software on a set of "bad" sample images that represent various welding anomalies (including weld overlap) and a set of "good" sample images that do not have any anomalies. In this way, all anomalies, whether they are required or the main cause of rejection, can be identified as defects. In the second part of the inspection, engineers can use the classification tool to classify weld defects by category. By combining the defect detection tool with the classification tool, automotive manufacturers can ensure that the inspection system identifies all welds and successfully classifies overlapping welds.

Using the Segmentation technology of Solomon SolVision AI image platform, the image features of micro-scratches and dirt on the wafer are located and annotated, and then used to train the AI model. Even under the image background with grinding marks, AI vision can still easily detect deep and shallow micro-scratches and other dirt defects, and accurately detect the location and area of the defects.

Due to the high temperature and heat of the welding factory, basic protection must be worn when entering, and the defects of the weld are complex and irregular. It is not easy to maintain the quality consistency by relying on artificial experience to detect the weld.Using SolVision's Segmentation technology, the appearance of normal welds, too thin or too thick welds and no welds are learned to train the AI model. Let the AI learn the characteristics of defects, and quickly detect whether the weld has defects. Pick out the defective welds for repair to effectively control the quality of the welding process.

1. Line Scan high-speed detection + AI defect classification
2. Support for front and back side appearance defect detection of Silicon/Glass Wafer
3. Applicable to CIS/IQC/OQC
4. Can be equipped with “8/12” EFEM, supports SECS GEM200/300

1. High-speed automatic defect photography based on KLARF file for分区/分Die/Defect size/Defect type
2. Provides AI real-time defect detection; instant detection and classification, processing speed up to 50 FPS or more
3. Can be equipped with “8/12” EFEM, supports SECS GEM200/300
4. Defect photography and detection of wafer/packaging process QA inspection stations/CP & FT

1. AI real-time defect detection and classification, real-time detection and classification, processing speed can reach more than 50 FPS
2. Can be equipped with “8/12” EFEM, supports SECS GEM200/300
3. Min defect size ≧ 0.3µm
4. Can be equipped with a yield management system

1. Two bottle sizes can be used on one machine: Supports two vial bottle capacities (10ml and 20ml) at the same time, no need to change tooling when changing lines, operators can get started directly, and easily switch between different bottle sizes.
2. Tray separation and loading + automatic vial loading: Apacer's exclusive design of Tray automatic separation mechanism can automatically separate the Tray trays that were originally stacked together to save space, and place them independently into the production line; combined with the bottle pushing and vacuum bottle picking mechanism, it can be used at once Dozens of vial bottles are automatically placed in the tray, which greatly improves the overall packaging efficiency and productivity.
3. Highly customized: Designed for customers' existing products, production lines and tray bottles, saving transformation and transformation costs.
4. AOI label optical inspection station: Integrate existing front-end and back-end equipment to detect whether the bottle label is completely attached, whether the label position is tilted, and whether the batch number is printed correctly; once a defective product is detected, the mechanism will automatically exclude it to avoid the defective product from entering the next station.
5. Customized database: Reserve the flexibility of upgrading ESG IoT smart devices.

1. High-speed inspection with a maximum inspection capacity of 60PCS/second (2M).
2. AI deep learning identifies defects.
3. Simultaneous inspection quantity can detect up to 10 areas at the same time.
4. Inspection area 2mm2-200mm2.
5. The inspection camera uses a 2M-25M high-speed area camera.
6. The inspection accuracy camera is equipped with a lens resolution of 0.001mm-0.05mm.
7. Measurement capability can detect 2M 60 frames/second or 5M 4 frames/second per second.
8. Detailed measurement data classification is stored in different directory folders according to camera sequence, by date and order. Automatically create file names based on date and time.
9. NG photo processing automatically generates directory folders and automatically creates file names based on date and time. During the inspection, click on the NG thumbnail or tile in the screen, and the complete photo can be popped up.
10. Real-time measurement trend chart can display the data of the last 1000 points of each axis.
11. The operation mode has operation mode (only display data), engineering mode (including real-time images) and commissioning mode.

1. When imaging, the laser light is projected onto the wheel in a uniform line to ensure that the image has a constant brightness.
2. To ensure that the camera receives enough laser light, a high exposure time is required. This can easily lead to stray light from the environment being mixed into the image, but Matrox MIL can effectively remove noise from the image and extract the necessary details.
3. Handheld high-precision instrument testing has shown that TreadVIEW can achieve an accuracy of 0.5mm on moving vehicles, with a single image inspection time of approximately 0.5-1 seconds. In terms of specific implementation data, if ±0.5mm measurements can be made within a ±10mm field of view, the system will be able to accurately recommend whether immediate or future remedial action is needed.

Automatic inspection of external defects and dimensions of Chinese herbal pills, Chinese herbal tablets, health pills, etc., such as shape, damage, cracks, color difference, foreign objects, stains, etc., with high-precision screening requirements

1. Resolution and defect detection capability range can reach 1.5um~5um
2. Zero dead angle detection area, can achieve 100% full panel coverage
3. Excellent autonomous image detection technology can support all sizes and different touch image designs
4. Intelligent defect classification function
5. Provide professional and customized services

Compact 3D vision solutions must be easy to use and provide software for internal technical personnel to integrate, manage, and maintain. The In-Sight 3D-L4000 works seamlessly with In-Sight software, which is based on a familiar spreadsheet programming methodology. Experienced and novice technicians can quickly and easily set up vision tools. The software then takes over, determining each blob above and below the set plane and highlighting features using a simple user interface (UI) that clearly communicates the health of the production line to every user. True 3D edge and blob tools, as well as other tools designed specifically for 3D imaging that can deliver accurate and consistent results at high speed and high quality, are easy to change and maintain.

Cognex Deep Learning is ideal for visually inspecting containers for defects before they are sealed and shipped. The defect detection tool can be trained on a small set of images of cartons properly filled with the correct number of tea bags. The defect detection tool can then identify any anomalies in the carton, including misalignment, quantity, orientation, or errors in visible strings and labels. The identified cartons can then be sent back to correct the errors and repackaged.

1. Cognex's Deep Learning tools apply intelligent algorithms to learn the difference between normal structural layers and defects, enabling more effective detection of tiny cracks.
2. Highly accurate detection can save good chip packages that may have been incorrectly classified as defective (NG), thereby increasing yield. Deep learning can find tiny cracks on WLCSP packages that may pass traditional inspection methods but fail prematurely in the field.

Teledyne DALSA's Sherlock machine vision inspection software and multiple industrial cameras can be used for real-time online inspection of glass bottles to remove bottles with bubbles.

Cognex Deep Learning excels in solving problems associated with mass tablet detection, achieving high precision levels. Through comprehensive training with tablets captured from multiple angles, the defect detection tool can subsequently detect any abnormal tablets omitted from initial training sets. All compliant tablets proceed to primary packaging seamlessly.

Cognex In-Sight 2800 provides user-friendly and cost-effective solutions for tobacco manufacturers conducting crucial inspections, featuring Edge, Contrast, Color, Pixel Count tools to pinpoint fiscal stamp attributes and relay pass/fail results to programmable logic controllers (PLC). Integrated lighting facilitates necessary contrast for luminous and low-contrast features, ensuring precise assessment of test subjects inline at production rates. Avoidance of rework, exorbitantly priced waste, and unnecessary returns ensues. Seamless setup and maintenance integrate In-Sight Explorer Software and EasyBuilder configuration environments.

1. Full Chinese operation interface
2. Applicable to various shapes of tablets
3. Statistical functions (total inspected/qualified/defective quantities)
4. Defective image storage and classification
5. Ability to adjust settings during inspection
6. Complete rejection mechanism planning (low/medium/high speed, contact/non-contact type)

Tablet and capsule inspection and screening machine can automatically and accurately detect tablets, capsules, hard capsules, soft capsules, etc. With simple settings, you can easily and accurately inspect various shapes, colors, foreign objects, stains and other external defects.

By using the In-Sight vision system, food and beverage manufacturers can ensure that labels are placed in the correct position on the product and avoid product recalls due to quality issues or damage to brand reputation. Similarly, automated inspection can identify incorrect labels before they cause further problems along the supply chain due to misaligned labels. Label alignment inspection can be used in conjunction with optical character recognition (OCR) and other In-Sight vision tools to ensure overall label readability and compliance.

Cognex Deep Learning is equipped with High Dynamic Range Plus (HDR+) technology, offering uniform illumination and deeper depth of field without requiring expensive and elaborate lighting systems, making low contrast defects on actuators clearer and more visible. The difference between HDR+ and standard HDR lies in its ability to quickly capture single shots of moving parts during operation compared to traditional HDR methods, which require objects to remain stationary and collect several images to achieve similar results.

Cognex deep learning's defect detection tools train using a small group of sample images to learn the standard appearance of syringes, allowing them to recognize slight deviations indicating needle protrusion situations while accepting changes in the surface appearance of syringes.

Cognex Deep Learning can detect many subtle microscopic slanted needle tip defects using a small dataset of sample images to train the defect detection tool. When magnified significantly, any variation presented in the light path reveals the structure of the needlehead surface. A highly reflective appearance indicates smoothness, whereas dimmed opacity suggests possible defects. This same process also highlights the internal and external diameters of needles for size checks.

1. Syringe Bevel Inspection Suitable for Cognex Deep Learning due to Training Using Multiple Angles; Despite numerous and intricate changes, transparency, and complicated geometry—including minuscule defects overlooked by human inspectors—these differences can still discern acceptable versus unacceptable curvatures.
2. Cognex Deep Learning Defect Detection Tools Easily Adapt to Subtle Shape Variations Arising from Supplier Changes, Resulting in Minimal False Rejections Compared to Traditional Machine Vision Requiring Major Programming Redesigns

SSI Sprocket Series is a continuous image screening machine that is suitable for products that can be hung and have a head diameter greater than 3mm than the rod diameter.

Socks have a variety of defect forms, including snags, wrinkles, and tears. The shape, size and position of these defects are not fixed. Traditional AOI is suitable for the inspection of whole pieces of cloth, but it has difficulty in detecting defects that are not fixed, and it is easy to make false detections. Therefore, manual re-inspection is still required.By collecting images of sock defects such as snags and wrinkles, and using SolVision's Segmentation technology to complete the training of the AI model, it is possible to quickly and accurately find defects, classify different defects and remove defective products. This can help to control product quality and improve production efficiency. By classifying and analyzing defects, it is also possible to optimize the overall manufacturing process.

During the reflow process, excessive solder paste or printing offset may cause solder balls to short circuit. In the past, such defects were mostly detected by manual visual inspection, which was inefficient and affected the production efficiency. Since the flow pattern of excess solder paste under high temperature is unpredictable, it is also difficult to detect by traditional optical inspection AOI.Using the Instance Segmentation technology of Solomon SolVision AI image platform, the reflow short circuit defects in the image samples are located and annotated, and then used to train the AI model. The trained model can easily detect the short circuit between adjacent solder balls.

Cognex Deep Learning can quickly and easily detect any anomalies in seatbelt fabric. It is trained on a small set of images of qualified webbing and stitching patterns. The defect detection tool can then immediately detect any errors in the pattern and stitching of the webbing or stitching. If a new webbing or stitching pattern design is introduced, the tool can be trained on a small set of images of the new design and will quickly learn to accept the design, without requiring a long downtime.

Cognex Deep Learning software can perform automated defect screening for a larger portion of the wafer. The defect detection tool can completely ignore the underlying wafer layer, even finding small defects anywhere in the wafer layer, and then remove any outliers. It can also be used in a two-tier inspection system to identify ambiguous cases and send them to an offline manual inspection station for further review.

1. Customized inspection requirements for different processes
2. Precise quality judgment and classification of inspection results
3. Inspection of product defect distribution map and individual defect features
4. High-speed, non-contact, 3D/2D surface morphology measurement

1. Inspection accuracy with an accuracy of 99.9% or higher.
2. OK inspection missed inspection rate is below 0.1%, and NG over kill rate is below 3%.
3. The training module takes about 3 months.
4. The system has the function of marking the inspection of the entire board and displaying the corresponding OK/NG tile.

A range of cameras can be used to capture images of welds for analysis. While 3D cameras may be required to measure weld volume, 2D cameras are capable of providing all the images required for other defect detection, and can ensure that spot welders are correctly positioned before the process begins.

Cognex's Deep Learning defect detection tools can find unacceptable and varied coating defects on wafer surfaces, which would be too complex or time-consuming for rule-based machine vision systems. The tool inspects the wafer surface, detecting if any cracks, chips or stains are present across it. It is trained on many different images showing variations in defect types and locations to identify potential areas of interest for inspection. Cognex's Deep Learning classification tools then classify the defects (e.g. cracks, chips, particles, etc.). This information can be used to improve processes to reduce defects and increase yield.

Apply the Segmentation technology of Solomon SolVision's AI image platform to build an AI learning module to automatically learn and detect the characteristics and location of crawling glue and overflowing glue. Combined with data augmentation technology, simulate the possible situations of adhesive overflow, so that AI can learn more feature patterns to improve accuracy. On the other hand, increasing the number of correct categories can improve the recognition strength and effectively reduce the interference of environmental factors.

Allied Vision Manta cameras comply with the GigE Vision communication protocol and support GeniCam functions. They can be cascaded to connect multiple cameras for 360° full inspection without dead angles. This case uses 12 Allied Vision Manta industrial cameras to set up 6 inspection stations to perform defect inspection of screws and fasteners used in automobiles or aerospace. The imaging speed is fast, and up to 500 can be detected per minute.

Semi-automatic inspection equipment, fully automatic discharge, high-speed and high-precision detection, precise defect marking, authority management, size confirmation, appearance inspection, intelligent inspection data statistics, non-conforming product analysis, report output, support for remote calibration management

SCI series is a continuous screw cap optical image screening machine

Engineers can use the Cognex Deep Learning defect detection tool in unsupervised mode to train the software on a set of "good" airbag images to create a reference model of the airbag. All features that deviate from the normal appearance of the model will be depicted as anomalies. In this way, Cognex Deep Learning can reliably and consistently detect all anomalies, such as pinholes, cracks, holes and unusual stitching patterns. It quickly identifies and reports areas of fabric defects, completely eliminating the need for expensive defect databases.

Solomon combines machine vision and artificial intelligence to use SolVision's Segmentation technology to train AI models for the various textures and shapes of white and transparent plastic parts. This can effectively detect assembly errors of plastic parts, improve the efficiency of defect detection, and make the overall process more perfect.

1. High-speed inspection with an inspection capacity of 30 meters/minute.
2. Maximum inspection width 360mm.
3. Maximum roll diameter 450mm.
4. High-resolution line scan camera uses color 8K and black and white 16K.
5. Learning function has AI artificial intelligence function to learn to identify OK/NG products, and then inspect defects.
6. Inspection capability can detect defects of 0.12mm2 for color and 0.06mm2 for black and white.
7. Defect threshold can set the defect length and width threshold, and all above the threshold can be detected.
8. Defect marking has defect marking capability, and you can choose to use a brush or laser marking.
9. Management function has management level, engineer and operator level, and can set operation permissions.
10. Real-time display has real-time display of scan image thumbnails and full images.
11. Automatic recording can record the number of defects, and the statistics table and curve chart are displayed on the screen.
12. Defect photos can be selected whether to save the defect photos.

RGI Double-Sided Series uses conveyor belt flipping or double glass disc inspection to simultaneously inspect double-sided defects, but defects may not be screened out due to refraction or dirt through the glass.

1. High-speed inspection with a maximum inspection capacity of 1000PCS/minute.
2. Inspection turntable diameter 250mm-700mm.
3. Standard machine size 850mm×width 850mm×height 1800mm.
4. High-resolution area camera uses 1.3M-25M.
5. Learning function has AI automatic learning function to identify OK products and then check NG defective products.
6. Inspection capability can detect defects of 0.01mm2 for color and 0.005mm2 for black and white.
7. Defect threshold can set the defect length and width threshold, and all above the threshold can be detected.
8. Defect marking has defect marking capability, and the selector can be used to separate the materials into OK/NG/NULL three hoppers.
9. Management function has management level, engineer and operator level, and can set operation permissions.
10. Real-time display has real-time display of inspection thumbnails and full images.
11. Automatic recording can record the number of defects, and the statistics table and curve chart are displayed on the screen.
12. Defect photos can be selected whether to save the defect photos.

Cognex robust modular In-Sight 7800 series machine vision systems tackle this application using multiple tools (histograms, edges, etc.) complemented by externally mounted linear lights above mouthpieces. In-Sight 7800 detects contrast disparities between mouthpiece and filter paper, validating presence and proper placement. Edged tools scrutinize recessed mouthpiece tail ends for incisions, segregating rejects from fast-paced production lines. Efficient installation follows suit, accompanied by simple configuration enabled via In-Sight Resource Manager software. Network-enabled Human-Machine Interfaces monitor test outcomes remotely.

The colorful nature of ribbons makes automatic optical inspection difficult. Due to the complex fabric patterns, it is difficult to find specific feature points. Automatic optical inspection (AOI) is prone to missed detection or misjudgment of defects due to changes in patterns and colors.Using the Segmentation technology in SolVision to detect ribbons of various colors and patterns can accurately find the location, size and shape of defects such as holes and loose threads. Both the detection speed and accuracy can meet the standards. By recording and analyzing the appearance of defects, it is possible to trace back the problems in the manufacturing process and improve the product process.

1. Common appearance defect inspection of textile fabrics: broken warp, hairiness, color difference, dirt, white spots, creases, indentations, damage, etc.
2. Inspectable fabrics: plain weave, knitted fabric, glass fiber fabric, non-woven fabric, bonded fabric and brushed fabric