Solomon’s SolVision provides an AI-embedded solution for visual quality control in the production of embossed plasterboards, an extensively used construction material. Manufacturers often create innovative designs on plasterboards to promote usage. This creates intricate embossing, making quality inspection challenging due to the complex patterns. The SolVision platform utilizes segmentation technology to identify dirty stains, oversized embossing, unclear patterns, and other subtle defects on the plasterboard, which would be difficult to recognize via traditional optical inspection or human intervention.

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.

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.

Solomon combines machine vision and artificial intelligence to create AI learning modules using the Classification function of SolVision AI image platform to judge the precipitation conditions from the image features in the database. Through deep learning technology, it can identify the precipitation conditions of liquids of different colors and accurately distinguish 7 different precipitation patterns, thereby judging the quality of the contents.

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.

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.

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.

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.

Tires go through many high-pressure, high-load and high-temperature processes during the manufacturing process. The dust generated by the on-site machines and materials, coupled with the long-term operation of the printing process, make the surface of the inner tube blurry and the color shades uneven, affecting the recognition of the inner tube coding. After mass production, it is not conducive to manual recognition. If traditional AOI detection is used, it cannot be effectively identified in the case of unclear digital printing.Using Solomon SolVision's Segmentation technology, the numbers and shapes of the tire inner tube codes are photographed and trained for AI models. Then, optical character recognition (OCR) is used to accurately identify the codes. Even for incompletely printed or lightly colored characters, they can all be successfully identified, effectively improving the accuracy of code recognition.

Engine numbers are printed on the engine by branding. When taking images of engine numbers, they are also easily affected by shadows, resulting in uneven brightness of fonts and backgrounds, which makes it impossible to read the numbers by machine. Even with artificial visual inspection, it is still difficult to quickly identify the codes on the engine on the production line.Using the Segmentation technology of Solomon SolVision AI image platform, the model is trained with image samples of different brightness and optical character recognition (OCR) is performed to convert the engine number in the image into numerical information, which is immediately logged into the original database system and linked to the VIN.

Use SolVision's Feature Detection feature to learn the location points that need to be identified on the tray, and then use Segmentation technology to perform optical character recognition (OCR), which can greatly optimize the traditional AOI workflow. It is not restricted by the displacement, skew and character defects of the identification screen, and can accurately identify the source of individual materials. With the increase of the number of learning pieces, the ability of AI to identify characters can also be continuously optimized, making character identification no longer difficult.

Solomon combines machine vision and artificial intelligence to use the Feature Detection tool of the SolVision AI image platform to define the characteristics of the assembly positions of each component in the PCBA layout, and train the AI model with the defined image samples. Through the trained AI model, it can instantly detect abnormal conditions and locations such as missing components or assembly errors.

When using traditional automatic optical inspection to detect plastic defects, it is difficult to quantify the defects due to the variety and changing positions of the defects. It is easy to encounter the problem of insufficient defect samples, which makes it difficult to quantify the defects. This leads to insufficient detection accuracy. If manual inspection is maintained, the detection speed is relatively slow and the quality is inconsistent. There are still many difficulties in identification.By using SolVision's Segmentation technology, a defect database is established for the shape and color of rubber product defects, and then AI is used to learn the characteristics of the defects. This can identify defects of various types and positions. With the increase of learning images, the ability of AI visual inspection is continuously optimized, which significantly improves the accuracy of rubber defect identification and effectively solves the problem of unstable detection of rubber product defects.

The internal components and circuits of power supplies are diverse and complex. When detecting connections, they are easily affected by background interference, which affects visual judgment. On the other hand, wires are deformable materials and can be arranged and stored in different ways depending on the assembler. These factors make it difficult for both manual and traditional optical inspection to be performed, making it difficult to effectively control product quality on the production line.Using Solomon SolVision's Segmentation technology, the correct and incorrect feature patterns are defined according to the wire color and terminal block assembly conditions in the image, and the AI model is trained. The trained AI model can accurately detect and locate wire misconnection defects and identify defective products in real time.

In the production of fasteners, the most common injection molding defects are mold release agent oil stains, white spots, burrs and debris, of which oil stains are the most difficult to detect. White spots, burrs and debris have obvious features in the image, while products with oil stains are very similar in appearance to general good products, which are difficult to detect.Using the Segmentation and Classification technology of Solomon SolVision AI image platform, deep learning is performed for each type of surface defect. After the AI model is trained, it can immediately detect all types of defects including oil stains.

Using SolVision AI image platform's unsupervised learning tool Anomaly Detection, AI deep learning is performed with non-defective image samples (Golden Sample), and data augmentation technology is used to improve the AI model's recognition of standard samples. The trained AI model can identify the differences between the tested object and the standard sample, locate and mark the position of the micro-crack defect inside the packaged chip, and is completely unaffected by the characteristics of the penetrating image.

By using Solomon SolVision's Anomaly Detection Tool unsupervised detection tool, the images of PCBA Golden Sample are learned to train the AI model. It can identify the differences between the PCBA to be inspected and the Golden Sample and mark them as defective, which greatly improves the inspection efficiency.

Solomon combines machine vision and artificial intelligence to use the Segmentation technology of the SolVision AI image platform to locate and annotate scratches, dirt and other abnormalities and defects on the aluminum substrate in the image sample. Through AI deep learning, it can automatically and instantly detect and locate various defects on the aluminum substrate, greatly improving the production efficiency of the production line.

SMD capacitors are small in size, and it is not easy to pick and place them. To observe defects, it is necessary to observe them under microscopic tools. And because MLCCs are very fragile, the inspection process must also be very careful, which increases the difficulty of inspection.Using SolVision's Segmentation technology, the shape and location of defects on the protruding part of the electrode are learned, and an AI model is established. After the AI learns the characteristics of the defects, it can quickly detect the defects on the protruding part of the capacitor, which greatly improves the overall yield of the process.

In addition to judging whether the packaging is sealed, in order to find the root cause of the problem, it is necessary to further confirm the type and cause of the incomplete seal. However, because the different types of sealing defects are very different, and the surface of the object is highly reflective, it is not easy to find and classify the defects with either the naked eye or automatic optical inspection (AOI).Combining machine vision and artificial intelligence, Solomon uses the Classification tool of SolVision to define the state of good seal from the image and compare it with multiple defects, including incomplete bottom seal, no seal on both bottom and side, and incomplete seal on both bottom and side. It can instantly detect incompletely sealed packages and classify the defects.

SolVision's Segmentation technology performs optical character recognition (OCR), which is different from the traditional AOI workflow. It is not limited by the object background color, ambient light and multiple character types. It can accurately identify individual codes, and with the increase of the number of learning samples, it can also continuously optimize the AI's ability to identify characters, making character recognition no longer difficult.

Metal case scratches are very fine. Under normal light, it is difficult for personnel to detect defects visually because the metal material is easy to reflect light, which can easily lead to poor appearance quality problems.By using SolVision's Segmentation technology, a defect defect database is established for the appearance shape of defects, and specific defects are classified, such as obvious defects, fine defects and extremely fine defects. Deep learning is used to identify obvious defects and ignore acceptable minor defects. , Effectively improve the detection accuracy and speed, and ensure that the finished products on the production line can enter the assembly process without any defects.

Metal stamping parts can have a wide variety of defects in different shapes and sizes, and oil and water stains are even more difficult to observe. On the other hand, the brightness of the workpiece also varies during imaging, which makes it very difficult to perform defect detection.By using the Segmentation technology of SolVision AI image platform, AI models are trained with images of various defects with different brightness. After training, the AI model can easily detect various defects on stamping parts, such as: shallow scratches, oil stains, water stains, burrs, etc., which greatly improves the surface quality of the product.

During the assembly process, there are occasional human errors that can lead to products with screws not fully tightened or with gaps in the accessories. For such repetitive assembly defect detection, the introduction of automation will quickly improve product omission problems and further improve the efficiency of manpower allocation.By applying Solomon SolVision's Segmentation technology, the image of screws and other assembly positions is located, and the preliminary identification and classification of the assembly dovetail degree is performed. The AI model is trained to quickly identify the completeness of the assembly of electronic components. With the increase in the number of image samples learned, its detection efficiency can also be continuously optimized, effectively improving the product quality yield.

By using the Segmentation technology of SolVision AI image platform, AI models are trained for the image information of the name, concentration, and capacity on the IV bag body. The image features are learned to quickly identify and classify various types of infusion products.

1. An innovative automatic optical inspection station with two 2D Basler cameras located under the glass screen. When foreign objects in the IV bag fall to the bottom, the lighting system located above the IV bag allows SolVision's complex artificial intelligence algorithm to detect foreign objects.
2. SolVision successfully detected all foreign objects with 100% detection accuracy
3. The inspection cycle is 500 milliseconds per bag, exceeding the customer's target
4. Successfully detected and significantly reduced overall inspection time, exceeding customer expectations

Using the Segmentation technology of SolVision AI image platform, the defect features in the image samples are annotated and used to train the AI model. The trained AI model can automatically detect and mark the location of the grain edge fracture defect, which greatly reduces the risk of the chip breaking in the subsequent packaging process.

By using the Segmentation technology of SolVision AI image platform, the fine defects on the club head in the image samples are labeled one by one, and the AI model is trained. After the training is completed, the AI model can locate and mark all the fine surface defects regardless of the brand logo, design pattern and metal gloss.

Gradient glass bottles are all subjected to a sandblasting process to create a frosted finish. Common defect types during the manufacturing process are uneven color or black spots on the bottle body. These defects are difficult to detect using the automatic optical inspection (AOI) method because they cannot be clearly defined and their patterns are not fixed.Combining machine vision and artificial intelligence, Solomon uses SolVision to train AI models with glass bottle defect images. By using Segmentation technology to find and learn the feature conditions of defect images, after taking images of the bottle from all angles, the trained AI model can quickly detect the defect distribution of the glass bottle body from all angles and mark the defect locations.

By using the Segmentation technology of SolVision AI image platform, AI models are trained with image samples of bottle cap text and barcodes, and optical character recognition (OCR) is performed. This can accurately identify product information on the outer packaging in the high-speed beverage production line. In addition to detecting products with poor printing, it also greatly enhances the efficiency of traceability management and record retention on the production line.

There are many kinds of defects in the manufacturing process of hard disk brackets, including metal indentation, surface white fog, hole scaling, hole blackening, etc. It is not easy to detect them one by one through manual inspection. However, small defects may cause problems such as misalignment of pores during assembly.Using SolVision's Segmentation technology, the AI model is trained for the shape of defects on the metal bracket. After the AI learns the defect features, it can quickly detect various small defects on the hard disk metal bracket.

PTP packaging is mainly made of transparent PVC blisters combined with aluminum foil backing. However, the blisters are transparent, which makes it easy for light to be reflected in the fast-moving packaging production line, affecting visual judgment and causing the product packaging defect rate to be high.

At present, manual identification and registration of bicycle frame numbers are still used, which is labor-intensive and inefficient. If automatic optical identification (AOI) is used for character recognition, the stainless steel tube surface is a curved surface, and lighting is easy to cause reflection, making the The code on the curved surface is not clear. Whether manual or automatic optical inspection, it is more difficult to identify the characters on the curved, reflective stainless steel tube. Solomon combines machine vision and artificial intelligence to use SolVision Segmentation technology to train AI models for the gloss of the numbers on the stainless steel tube, which can achieve excellent optical character recognition results.

Since BGA solder joints are concentrated under the package, it is impossible to confirm the soldering quality by visual or traditional optical inspection methods after soldering. X-ray equipment must be used to penetrate and image to detect whether false soldering defects occur. X-ray images are grayscale images with background noise, and there are no obvious edges in the imaging, making it difficult to write logic to identify defects in the images.Using the Segmentation technology of SolVision AI image platform, the overlapping solder ball false soldering defects in the X-ray image are annotated and used to perform deep learning of the AI model. After training, the AI can accurately detect false soldering defects under the conditions of background noise and no obvious image edges.

The roof of a car is a streamlined shape, and its weld has a high and low drop, so the brightness of the images taken at each position is different. The randomly generated air hole defects also have different characteristics, so it is difficult to formulate rules for detecting weld air hole defects in this situation.Using the Segmentation technology of Solomon SolVision AI image platform, the weld air hole defect images of various brightness are annotated to train the AI model, which can detect air hole defects anywhere on the weld regardless of the image brightness.

Using Solomon's core AI machine vision system SolVision, an AI model can quickly and accurately identify the direction and angle of each artichoke, using its stem as a reference point, regardless of the size of the stem itself. SolVision also allows AI detection results to be exported through various communication protocols.

Use Solomon SolVision's Segmentation technology to learn about various types of defects, and at the same time set OK categories to avoid false positives and false negatives. With data augmentation, the scope of AI learning is increased. It can not only effectively detect various types of defects, but also accurately detect edge protrusions, black edges or black spots in cluttered or complex backgrounds. It also has a good recognition effect for less obvious defects.

Use the Segmentation technology of the SolVision AI image platform to perform defect identification, detect and mark various subtle defects in complex imaging backgrounds, so that users can monitor and eliminate abnormal conditions of the carrier plate in real time.

Laser welding is divided into two categories: laser heat conduction welding and laser deep penetration welding according to the power density. It has different weld seam characteristics. Due to the different welding positions and styles of the products, it is impossible to use traditional optical detection to distinguish the annular distribution of the weld seam, the appearance of the finished product such as no welding, etc., so the production line is finally inspected manually, which often causes the welding quality to be uneven.By using the Classification and Segmentation technology of Solomon SolVision AI image platform, AI models can be trained with weld feature images to identify welding power and no welding defects, and the number and distribution of fish scale patterns of weld seams can be accurately detected through deep learning.

Solomon combines machine vision and artificial intelligence to use the Segmentation technology of Solomon SolVision AI image platform to perform defect detection (Defect Identification). In the fast and mass production of fried food processing line, it can identify multiple different defect patterns and instantly detect defective products.

By using the Segmentation technology of SolVision AI image platform, the location of the crack defects in the image samples is located and labeled, and the AI model is trained based on this. After the training is completed, the AI can be used to detect the pores and cracks on the eggshell surface and then grade them, which can improve the safety and commercial value of egg consumption.

Different flavors of coffee are distinguished by capsules of different colors. One plate of coffee capsules is used as the standard group, and the other plate of coffee capsules is used as the control group. Using SolVision's Feature Detection tool, the images of each coffee capsule are learned. If any coffee capsule is placed incorrectly in the plate, SolVision can immediately detect the error and mark it with a box. It can successfully identify coffee capsules with high reflection and small color difference.

By using the Segmentation technology of Solomon SolVision AI image platform, the defects in the image samples are labeled and used to train AI models. After deep learning, the quality control department can accurately identify whether there are defects on the mask and eliminate the defective products.

By applying Solomon SolVision AI image platform's Segmentation technology, AI models are trained with various LED substrate defect image samples. After deep learning, AI can accurately detect and annotate defects. In addition, the Detect Region tool can be used to divide the field of view into zones. In addition to masking areas that do not need to be detected, it can also identify the area where the defect is generated to achieve the purpose of zoned detection.

The side threaded surface of metal threaded kits is easily damaged by collision during handling, or by the tool marks of the cutting tool during processing. Due to the fact that cylindrical kits are easy to roll and are small and difficult to hold, even with strong light and microscope equipment, it is still difficult to detect with the naked eye, and it is easy to miss and miss detection.Using Solomon SolVision, AI models are trained with images of metal kit defects, and Segmentation technology is used to learn the characteristics of tool marks and collision defects. After the AI model is trained, it can easily detect small metal defects that are difficult to identify with the naked eye. The kits are picked out to improve the overall quality of the shipped metal kits.

The situation of chip jumping in the wafer is random, and the resulting defect patterns are diverse and difficult to predict the location of the defects. For AOI, it is almost impossible to set logic for jump defects and detect them based on it.Using the Segmentation technology of SolVision AI image platform, the AI model is trained with image samples of defects such as stacking, missing materials, skewing and misplacement, and flipping. After the AI training is completed, it can easily and quickly identify and mark the positions of abnormal pick and place on the wafer.

Wafer cutting is a very important process in the semiconductor and optoelectronics industries. If the cutting process cannot maintain high yield, high efficiency and maintain chip characteristics, it will greatly affect the overall production capacity. The quality control of the wafer cutting saw is mainly through the detection of external defects. Common external defects include irregular patterns and multiple drills on the saw body. Since the wafer cutting saw itself has circular stripes, it forms a complex image background, which seriously affects the machine vision for defect detection.Using the Feature Detection tool of SolVision AI image platform, the irregular patterns and multiple drill defects in the image samples are annotated and trained to train the AI model. AI vision can then detect various defects on the wafer cutting saw body in real time.

Since die bonding technology is the key to the packaging process, it has high requirements for speed and accuracy. However, the texture of the process image is very complex. Traditional optical inspection cannot use logic writing to detect defects such as angle, displacement deviation and missing, which often leads to missed detection, misjudgment and wrong positioning, which greatly affects the production efficiency of the packaging line.Using the Solomon SolVision AI image platform to enhance the reliability of displacement and angle information, accurately detect the manufacturing errors and abnormal conditions of the die bonding system. On the other hand, the AI module can also extend the learning of different chip forms and perform analysis and detection for different types of packaging products.