The In-Sight 3D-L4000's unique optical scanning engine also offers several advantages over other competing solutions, including eye-safe operation, while providing more light to the surface to increase throughput, providing more accurate 3D point clouds for measurement values, and even 100% of the In-Sight 3D-L4000's field of view can be measured even if debris blocks 50% of the laser.

Cognex In-Sight vision systems with deep learning OCR solutions can confirm that lids and containers match each other and accurately reflect the contents of the package, as well as confirm that labels comply with internal procedures and quality standards enforced by regulatory agencies. Cognex technology ensures high-speed reading and decoding of barcodes and text in the most demanding environments.

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.

Cognex In-Sight® vision systems are used for tire assembly inspection, which is typically done by clicking a few times to set up the vision system to inspect the entire tire skin as the roller rotates to combine all the assembly layers. The rugged industrial design of the vision system allows it to perform precise and accurate measurement tasks regardless of the factory production environment.

Conventional machine vision systems can accurately calculate quantities of drugs and vials but lack flexibility and adaptability for certain error scenarios, unlike Cognex Deep Learning. Capabilities extend further than counting, encompassing misaligned, reversed, or color-confused containers, thereby improving Overall Equipment Effectiveness (OEE). Component location tools train with containers orientated towards various directions, resulting in consistent recognition across all possibilities, generating reliable counting methodologies considering peripheral distortions simultaneously.

Optical Character Recognition (OCR) and Optical Character Verification (OCV) capabilities read and confirm printed data, verifying the quality of assorted elements required to be imprinted, such as logos, dates/batch numbers, and graphics. Comprehensively utilized in Cognex's versatile In-Sight Systems, these code reading tools ensure precise decoding accuracy.

Implementation of Cognex Deep Learning proves effective in solving applications of this nature. Component positioning tools easily handle complex vaccine assembly tests, e.g., items placed in varied directions, overlapping, missing, or containing different combinations of SKUs. After training a deep learning system from multiple angles to recognize assembled components and discriminate between existing and newly introduced ones (even visually alike counterparts), successful identification becomes effortless.

Cognex Deep Learning presents a highly trustworthy medical patch testing solution, hinging upon measurable medication dosages against specified locations. Training occurs through mounting patches above several accepted droplet sizes and shapes against rejected liquid forms, creating classes outlining permissible drops' shape and magnitude. Ultimately, any remaining inconsistencies fall into the removal category.

Cognex Vision Systems paired with OCR technology can detect barcodes' existence and verify letter-number sequences, catering to stringent OCR demands inclusive of laser-etched marks or DPM texts. Cognex Deep Learning Solutions secure precise reading and authentication of barcodes. Moreover, deep learning offers OCR and character verification (OCV) functionalities that encode distorted, slanted, and poorly etched letters, given a pretrained multidirectional font database. No requirement exists for designing extra programs or font training since it readily identifies most textual content.

Cognex tire solutions use character reading vision tools to enable tire manufacturers to read codes with high accuracy even in the most demanding conditions. The character reading vision tools can accurately locate and read DOT characters, and adapt to the changing code appearance caused by variations in the molding process.

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.

Cognex Deep Learning can simplify and automate this identification process. The classification tool can learn to identify and classify different tire tread patterns from a set of training images. It then groups tires with rims according to their category, ensuring that the correct components are selected and installed on the vehicle.

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.

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

1. Cognex Deep Learning is ideal for the complex task of finding and picking good nuts that are oriented correctly and placing them on chocolate.
2. The classification tool is trained on a set of images of good and bad nuts. The tool can quickly classify nuts into acceptable and unacceptable categories.
3. The part location tool is trained on a set of images of good nuts that are oriented correctly. It can then locate the nuts as they pass by, regardless of the number of nuts present at the same time.
4. Combining these tools ensures that good nuts are identified and located so that they can be picked and placed on the chocolate by a robotic arm. Manual inspection or other forms of machine vision cannot achieve the same speed and accuracy for this task.

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.

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.

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.

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.

Cognex Deep Learning can read printed barcodes on difficult backgrounds. With a pre-trained font library, the deep learning OCR tool is easy to set up and deploy. It can then be trained on a small set of images of text printed on a variety of backgrounds, and it learns to identify the text while ignoring the background. This is even the case when the text appears on new backgrounds that were not in the original training set. When text appears on a new background, the OCR tool does not need to be retrained, which keeps the production line running without interruption or loss of read accuracy.

Cognex Deep Learning can read printed OCR codes on flexible plastic film, which are not only difficult to read, but the background can also vary due to many different cut parts or chicken. The deep learning OCR tool comes with a pre-trained font library, making it easy to set up and deploy. The OCR tool is trained on a small set of images of text that is skewed, angled, and distorted. After that, it can find and read this type of text on flexible food packaging, regardless of the product underneath the packaging.

Cognex Deep Learning can solve the orientation problem of liquid filling bags and other flexible containers during packaging and transportation. It can be trained on a set of images of possible containers with different orientation labels. The classification tool can accurately determine the orientation of each bag seen and provide the data to the picking and placing robots, which then pick and place them in the correct orientation in the secondary packaging containers.

Cognex Deep Learning can rapidly and easily tackle connector placement location detection challenges thanks to the Assembly Validation Tool. Through training with a set of properly functioning connection and contact images, it becomes proficient in understanding all correct installation changes—even when faced with glare and complex backgrounds post-training. Consequently, the validation tool accepts all suitable components across the entire line, while simultaneously eliminating ones falling outside parameter limits.

Utilizing the powerful yet compact In-Sight 8505P Imaging System, perform measurements for all dimensions of syringes. Equipped with High Dynamic Range Plus (HDR+) technology, In-Sight 8505P addresses complications stemming from glass reflection and refraction, as well as plunger stoppers and liquids. This imaging technique reduces lens flare and image noise, improves edge contrast, increases dimensional precision, and maintains short exposure times. The key distinction between HDR+ and standard HDR involves capturing single exposures of moving components at speed, whereas standard HDR requires static components and collecting multiple images for comparable results.

Cognex Deep Learning handles a wide variety of defects, making it the optimal solution for this application. The defect detection tool learns ink printing problems on curved surfaces and reflective surfaces of syringes before recognizing if ink is too heavy, too light, or dirty using pattern matching software paired with High Dynamic Range Plus (HDR+). This technology decreases glare, enhances contrast, and accelerates automaticized print inspection speeds. Distinguishing factors include quick single-exposure collection for moving components via HDR+, whereas Standard HDR demands immobility and multiple image acquisitions for comparable results.

Cognex PatMax technology and color tools can identify designs and stamps, regardless of changes in direction, angle, lighting, and other factors that can affect the appearance of items on the production line. PatMax uses a set of boundary curves to learn the geometric shape of an object and then searches for similar shapes in the image, without relying on specific grayscale values. Damaged or broken products are removed before being packaged or shipped, avoiding costly returns and protecting brand reputation.

Cognex's AI tools help minimize defects related to assembly processes in mini LED screen manufacturing, including solder volume and alignment of LED chips on bonding pads. The detection system uses a series of images representing both good and NG (defective) results during its training phase. It learns to tag notable defects while disregarding abnormal situations within acceptable tolerances. These tools are capable of precisely locating and identifying targeted inspection zones (ROIs) along with any potential critical defects present within those regions. Manufacturing managers can use this information to more efficiently manage the quality of displays, thereby reducing costs and increasing profitability.

Cognex Deep Learning can learn from a set of images of installed spring clamp pliers that are qualified and unqualified. The classification tool can quickly determine whether the clamp is working properly or identify potential problems that need to be corrected before further assembly of the vehicle can proceed.

Cognex Deep Learning provides an effective inspection solution that combines the human ability to identify subtle variations with the reliability, consistency, and speed of an automated system. Engineers can use the Cognex Deep Learning software's defect detection tool in supervised mode to train the deep learning-based software on a set of representative "good" and "bad" compression ring images. Technicians can add annotations to "bad" images where there are long scratches, and to "good" images with normal variations and allowable defects such as rust and small cracks. Based on these images, Cognex Deep Learning can learn the natural shape and surface characteristics of pistons, as well as the typical appearance of scratches.

Cognex In-Sight 2800 Series Smart Cameras fit neatly into confined quarters of tobacco manufacturer gear. Mounted above hopper bins, In-Sight 2800 sports embedded high-powered lighting rigs, distinguishing loose tobacco apart from pouches. Surface Flaw Tools match perfectly with Filter Program Tools, highlighting luminosity and contrast discrepancies indicative of perforated states especially handy for similarly colored brown tobaccos. Dependably inspecting vessel contents, In-Sight 2800 removes faulty chewing tobacco packs prior to reaching consumers' hands.

1. Solder paste inspection: Machine vision can inspect for slump, cleaning, bridging, and spikes. It can also be used to visually inspect solder paste location and shape to close the loop control of PCB screen printing process.
2. Surface mount device inspection: Machine vision can inspect for lead length, width, spacing, bend, lead presence, chip size, and ball location, size, and spacing.
3. Automated optical inspection (AOI): When visually testing assembled circuit boards, AOI inspects component placement and checks for missing, reversed, or incorrect components.

Traceability solutions ensure full compliance with food safety and traceability regulations by capturing images of codes at each scanning point and storing the decoded data in a central database. Cognex barcode readers can reliably read 1D and 2D bar code images with a 99.9% read rate, regardless of code quality or orientation. Image-based readers offer the speed and accuracy needed to ensure that all shapes and sizes of packages are properly sorted, picked, stored or shipped, and easily identified and located in the event of a product recall. In-Sight vision systems use AI-based OCR tools to read alphanumeric date/lot codes and store the information in a central database that can track and trace products throughout the supply chain.

Cognex AI technology combined with High Dynamic Range Plus (HDR+) technology offers an ideal solution for particle material detection. The Cognex AI solution trains using diverse microparticle substance types found inside pills and pill containers, accounting for varying shapes and sizes, whether air bubbles are present, and incorporates reflections and refractions seen through glass bottles and container windows. As a result, it effectively detects particles even under complex lighting conditions.

Rule-based visual systems face difficulty adapting to seal variations, opacity, or Tyvek materials. Nevertheless, Cognex Deep Learning Solutions serve as supplementary and alternative options. Deep learning reliably identifies foreign objects, invalid sealing, impurities, improper labels, and paint coating flaws detrimental to package integrity. Implementing 100% visual inspection achieves peak efficiency by minimizing operator error and providing instantaneous highlighting of concerns. Such highlights facilitate clear distinction of issues for personnel or machines, followed by subsequent categorization later.

Cognex Deep Learning can dependably examine medical kits bundled in packaging for potential defects despite the presence of components facing varied angles and construction diversity among tubes. By undergoing comprehensive kit image training, the Part Location Tool discovers and confirms necessary components' existence, regardless of numerous possible appearance modifications that could complicate assessment. Damage sustained during assembly leading to deviation beyond allowed change margins causes kit failure in final examinations.

Deep learning streamlines tasks involving automatic positioning, identification, and classification within a single image's multiple characteristics. Depending on various item dimensions, shapes, and surface attributes, the system discerns and groups distinctive features accordingly. Users can train assembly positioning and verification tools to find desired items. Afterward, the picture gets segmented into separate sections, allowing the tool to assess the presence of the required item and validate its kind, regardless of orientation and lighting settings. Furthermore, deep learning finds and identifies flyers inside boxes, averting recalls and assuring patient safety.

Combining smart cameras with deep learning software uses optical character recognition (OCR) to decode damaged barcodes. Thanks to the pre-trained font library of deep learning, the deep learning barcode reading tool in the software is ready to use out of the box, greatly shortening the development time. Users only need to define the target detection area and set the character size. When introducing new characters, you don't need to have visual expertise, and you can also retrain this robust tool to read application-specific barcodes that traditional OCR tools can't decode.

Cognex Deep Learning's text and character reading capabilities can reliably and accurately decode deformed, skewed, damaged, or low-contrast codes. This can be done by training on a set of OCR code images with different angles, light sources, damage conditions, and other variations.

Cognex's Deep Learning OCR tool can use a pre-trained built-in font library of over 1,000 characters to read curved strings, low contrast characters, as well as distorted, skewed, and poorly etched barcodes. The OCR tool also provides a re-training capability, allowing users to solve new or specific characters that cannot be automatically identified on the first pass. Quickly and accurately reading chip identification numbers not only improves traceability, but also ensures the correct information is captured for future reference when needed.

Cognex Deep Learning trains with numerous examples of successfully inserted needled nozzles within an acceptable range, alongside outliers marked as defects characterized by characteristics beyond the scope of acceptability, such as air bubbles, cracks, insufficient adhesion of connecting glue, problematic conical tips, or other inclusions. It flags these defects and eliminates them from the production line. Due to ease of training new needle lengths and measurement values, manufacturers avoid lengthy and complicated programming procedures required in conventional machine vision implementations.

1. Cognex Deep Learning quickly and reliably solves Printed Circuit Board (PCB) assembly verification challenges by undergoing training with sets of qualified vs unqualified PCB images. Three distinct deep learning tools operate seamlessly together on a single workstation for uniform testing of circuit boards without causing delays in production.
2. Assembly Verification Tools check if all components appear correctly positioned; meanwhile, Defect Detection Tools mark any solder problems, damaged locations on board-mounted components or other flaws. OCR (Optical Character Recognition) Tools read all characters on circuit boards and component surfaces, outputting them as text strings.

1. Cognex has specially developed Coated Optics Inspection (COI) machines dedicated to MLCC testing, featuring tailored lighting modules combined with advanced deep learning visual tools. Firstly, custom illumination modules specifically crafted for MLCC tests significantly reduce irrelevant surface fluctuations on capacitor bodies, making hidden defects apparent.
2. Following MLCC checks via Automated Optical Inspection (AOI) machines, COI machines ensure lower false report rates, fewer removed qualifying components from the production line, reduced manual checks, faster speed, enhanced accuracy, and valuable improvement insights in the processes.

Cognex's AI technology helps microLED manufacturers identify defective chips on display panels by being trained with a range of images showing both good and NG (defective) outcomes, enabling the software to skip over insignificant variations within tolerance ranges and instead flag major defects. This analysis tool scans specific areas of the panel, locating subtle imperfections in microLED components. Production managers can utilize a classification tool to categorize various defect types, optimizing upstream processes and boosting overall manufacturing efficiency. By detecting and resolving defects early in the process at an economically viable cost, this solution enables manufacturers to supply their customers with higher quality panels.

Cognex 2D vision systems with edge learning perform pass/fail inspection and reject defective items or packages when detected. The embedded edge learning tool can be trained with just a few images that classify packages as pass (all items present) and fail (one or more items missing). Even under shrink wrap, it can confirm that all bottles and products are present, helping food and beverage manufacturers prevent operational errors and maintain customer satisfaction.

Cognex's deep learning design has the ability to distinguish real defects from acceptable coating irregularities, addressing these complex detection challenges. Defect detection tools undergo extensive training involving different classes of glass bottles and multiple angles to thoroughly learn normal component variations, including the acceptable range of coating defects. Then, when analyzing drug bottles, they scan, evaluate, and label features outside the accepted range, all while minimizing false reports caused by coating defects.

The proposed solution combines conventional machine vision and deep learning visual systems, checking bottle caps from below and top-down perspectives, ensuring appropriate dimensioning and positioning, and revealing existing issues. Cognex Deep Learning can detect unexpected scrapes, holes, and other flaws while distinguishing simple cosmetic defects from functional shortcomings. Applying these technologies leads to improved quality, diminished unnecessary waste, increased productivity, and elevated yields.

Cognex Deep Learning solutions enable precise part localization, thorough problem analysis, and robust classification abilities, preventing deficient products from entering supply chains. Combining human-like detection skills with computerized automation and repeatability features ensures maximum functionality alongside robot collaboration, guaranteeing optimal performance in tandem with visual instruments. Detecting complex anomalies missed by operators reduces recall events, lowers rework expenses, and fully collects traceable images throughout operations.

Utilizing machine vision and deep learning techniques, mask manufacturers can ensure compliance with ISO standards during production and discover flawed masks before shipping. The Cognex In-Sight 8402 Visual System detects earloop and headband weld points in mask components while measuring mask width to confirm manufactured dimensions meet expectations. Although many defects might prove elusive and hard to predict, traditional machine vision algorithms struggle to account for them. Fortunately, with just fifty sample images, Cognex Deep Learning can effortlessly locate cracks, stains, sewing faults, and other irregularities, subsequently categorizing them.

Cognex's AI visual system and software assist manufacturers in identifying and classifying genuine LED chip defects through training with a series of images representing good and NG (no-good or defective) results. The software is then able to mark only significant defects within the target inspection area (ROI), which the defect detection tool identifies. Following this, the classification tool categorizes the defects based on the information gathered. With this information, production managers can increase the yield rate of high-quality LED products, address and solve production issues by utilizing classification data, ultimately enhancing profitability.

Using the blob counting tool, the In-Sight 2000 sensor can detect the presence or absence of relevant liquids. When the bottle is underfilled, there will be too few blobs displayed, and when the bottle is overfilled, there will be too many. Bottles that fail the inspection will trigger the rejection function.

Traditionally, rule-based machine vision systems used with automated optical inspection (AOI) systems do not perform well. Detecting potential defects (NG) through deep learning can enhance the reliability of the inspection process. The AOI machine uses Cognex Deep Learning tools to identify potential NG situations and provide those images to the system. The defect detection tool can dynamically capture regions of interest, while the classification tool can categorize different types of defects, distinguishing between defective and acceptable wire bonds. Categorizing defects not only helps identify process issues to avoid costly rework downstream, but can also successfully identify defects at the micron level, improving IC chip yield and lifetime performance.

Defect Detection Tools excel at detecting a wide array of defects, including but not limited to, soldering voids, bridging solder material, missing parts, misaligned components, and even minute errors invisible to human inspectors. Once detected, these defects are highlighted visually on the image for further processing and examination.

The In-Sight vision system, combined with feature extraction technology, uses lighting and software algorithms to create high-contrast images that enhance the three-dimensional features of components. It can capture errors and defects such as torn, cracked or deformed labels. Monochrome and color models can identify color errors and inspect the consistency and quality of labels in terms of size, shape, color and material. This quality control measure can reduce errors, help meet label quality standards and ensure customer satisfaction.

Cognex Deep Learning can easily confirm the presence and absence of straws without damage. It can be trained on a small set of images of undamaged straws, as well as a set of images of various unacceptable missing, damaged, or misplaced straws. The classification tool learns to ignore any background and classifies all images into acceptable or unacceptable states. Since there is no need to identify and define specific defects, the classification process is very fast. It only needs to judge acceptable/unacceptable.

Cognex Deep Learning provides a simple solution that can identify all anomalous features without even having to train on "defective" images. Instead, engineers can use the defect detection tool in an unsupervised mode, training the software using samples of "good" images. Cognex Deep Learning can learn what normal wire bond and lead appearances and positions should be, and flag any deviating features as defective.

Cognex Deep Learning tools can help verify the difference between OK and NG probe marks, making probe mark detection easier and faster. The software is trained on a wide variety of images, including images showing correct probe marks and images showing unacceptable probe marks. Unacceptable marks can then be classified as "pressure related" or "off center".

Cognex's Deep Learning tools can help manufacturers identify and classify true mold compound defects. This advanced vision solution uses a set of training images showing good and defective (NG) results, allowing the software to ignore anomalies within the margin of error and flag actual critical defects. Cognex's location tools can identify regions of interest (ROI). Once the ROI is defined, the defect detection tool identifies defects within that area. Then, the classification tool categorizes the different types of defects. With this information, production managers can not only improve yields of good ICs, but also use the classified data to diagnose and correct production issues, increasing profitability.

Cognex Deep Learning can reliably detect these types of products to prevent defective items from reaching customers. It is trained on a set of images of good items and another set of images of items with various types of coating damage. The classification tool can then quickly classify chewing gum or other items into acceptable and unacceptable categories. This level of quality cannot be achieved with other types of visual inspection.

Cognex's AI-based solutions can help high-power LED manufacturers identify and classify significant packaging defects. We train this advanced vision solution using a set of images representing good and defective (NG) results, allowing the software to filter out anomalies within the acceptable range and only flag relevant defects. The location tools can identify the regions of interest (ROI) to inspect. Once the ROI is defined, the defect detection tool identifies any major defects within that area.

Cognex Deep Learning is the ideal solution for detecting small defects on the necks of glass bottles. It is trained on a set of images of acceptable glass container necks. The defect detection tool can then identify anomalies such as nicks, inclusions, and cracks, while accepting a wide variety of potential glass neck appearances.

Cognex vision products with color and shape identification tools can accelerate the sorting process and prevent errors. They use color and pattern matching tools to identify components and detect defects, including damaged components and missing features.

Cognex Deep Learning is trained on a series of different images with different angles and rotation directions, and the classification tool can reliably classify painted colors. Then, when making a selection, Cognex Deep Learning will examine the entire image and appropriately weight the various changes, reflections, refractions, granularity and chromaticity within the image to select the best match.

Cognex Deep Learning can reliably identify foreign objects, invalid seals, contamination, and many other problems that can affect product integrity. 100% vision inspection can reduce operator error and achieve maximum efficiency. Deep learning can also further highlight problems in real time, allowing operators or machines to clearly identify problems, which can be classified later.

Cognex deep learning can instantly locate, analyze, and classify complex contamination issues, preventing large-scale production line contaminants from entering the supply chain. Deep learning combines human-like detection capabilities with the automation, scalability, and repeatability of a computerized system. This capability can be further enhanced by using robots to ensure that machines work together with vision tools to detect the most complex anomalies that operators sometimes miss. The end result is a reduction in recalls, reduced rework costs, and complete product image capture and full traceability.

VisionPro Deep Learning is highly customizable software that uses artificial intelligence (AI) to analyze thousands of annotated images to detect defects in challenging environments, such as the surfaces of cylindrical and prismatic battery cells. EtherInspect is a VisionPro-enabled vision software that uses built-in templates and tools to accelerate deployment. When used with modular 2D hardware like the In-Sight D900, these solutions allow users to quickly process EoL electric vehicle battery inspection. When robust defect detection is more important than speed, 3D solutions like the In-Sight 3D-L4000 can provide more accurate and precise measurements and surface inspection.

The short exposure time of Cognex line scan industrial cameras (CICs) makes them ideal for high-speed continuous operations such as electrode coating inspection. Combined with VisionPro deep learning software, the solution can isolate subtle defects in poor contrast environments, such as matte black electrode coatings.

Cognex Deep Learning uses color cameras to accurately verify that winding processes have been completed flawlessly. The defect detection tool can learn from a set of training images that includes images of zero-defect winding, as well as images that are labeled for a variety of other possible defects that can appear in different locations, including overlaps, misalignments, and crossovers.

Cognex Deep Learning tools provide an easier way to learn and classify chipping and burr marks, as well as distinguish them from normal cutting marks after the cutting process. The software is easy to train and can identify all chipping and burrs, classify them as acceptable or unacceptable, and ignore normal marks within the tolerance range.

Cognex's In-Sight 9912 Visual System delivers automated solutions for each box complete with distinct labels, such as emblems, names, or numerical codes. Once cargo arrives near the camera, In-Sight 9912 utilizes PatMax object localization technology, instantly spotlighting selected marks or motifs prior to computing quantities contained therein. Its expansive field of view and optical arrangement make it ideally suited for massive crates and distant checkpoints. Displaying results on tablet computers or laptops, flatscreen monitors, or web-based HMIs empowers operators to review examination outcomes.

Cognex Deep Learning performs exceptionally well in conducting X-ray inspection and verifications for assembled devices and packages. After training with valid device images containing intact components placed correctly, the Assemblies Validation Tool learns about accepted positional shifts across the whole product line and positions of diverse components. Post-training, the instrument promptly recognizes bends, incorrect placements, missing pieces, and drug quantity anomalies among packaged items while accepting completely assembled units meeting requirements.

The compact yet powerful In-Sight 8000 vision system can detect e-cigarettes to help tobacco manufacturers ensure their products are free of harmful debris. The In-Sight 8402 can be easily mounted inside the machine with an external light bar to evenly illuminate the inspection area and check the filter. Since the region of interest is relatively small, reducing the field of view (FOV) of the vision system provides the 2MP In-Sight 8402 with the extra resolution needed to clearly identify defects while keeping up with high-speed machines. Like all In-Sight, the In-Sight 8000 series can be quickly and easily configured and deployed using In-Sight Explorer software with the intuitive EasyBuilder configuration environment.

1. The component location tool can be trained on a small set of images of pizzas with the desired number of pepperoni slices or other toppings. It can then identify, locate, and count pepperoni slices, even if they are touching or overlapping, while ignoring variations in cheese and sauce underneath. If the number of toppings is not within the specified limits, the pizza will be rejected.
2. The defect detection tool can be trained on a set of images of acceptable pizzas, even with multiple different toppings. It will then detect any physical contaminants, including toppings that are not suitable for this type of pizza, while ignoring variations in cheese and sauce underneath.
3. The classification tool can be trained on a set of images of all types of pizzas produced in the factory, with labels. It can then distinguish between different variations and classify them so that they can be put into the appropriate packaging and accurately entered into inventory. These tools can be used all together to help manufacturers automate processed food inspection, ensuring that only the highest quality products leave the factory and end up on customers' kitchen tables.

The In-Sight vision system, combined with OCRMax technology, can detect the presence or absence of dates and batch codes, and verify the correctness of their alphanumeric chains. For demanding OCR applications, including DPM text that is laser marked, dot peened or chemically etched, Cognex AI OCR tools ensure accurate reading and verification of barcodes. These tools can use OCR and character verification (OCV) to decode deformed, skewed and poorly etched characters. A pre-trained omnidirectional font library can recognize most text without the need to design additional programs or font training.

Cognex Deep Learning can quickly identify pores under the same lighting conditions, while other methods are still trying to detect them. Engineers can use a set of "good" and "bad" cylinder images to train the software in a few minutes, using the mask filter program to adjust the region of interest and eliminate the bright circular plane with a hollow center. Technicians can use the Cognex Deep Learning defect detection tool in supervised mode to annotate the pores in the "bad" images and adjust the parameters, including feature size, aspect ratio, aspect ratio and shear modulus, to ensure that the created reference model takes into account all appearance changes. "Good" images that describe normal cylinders can help the software learn what kind of minor casting anomalies and variations are acceptable. Engineers can retrain the system, adjust parameters and add additional images until the model can summarize the normal appearance of the cylinder and identify abnormal conditions. At runtime, the deep learning-based software can detect each image in milliseconds, depicting features with pores as defects and other features as normal.

Cognex's VisionPro software provides a fast and accurate way to count Mini LEDs before packaging. Operators can easily train the software to identify, locate and count patterns of extremely small LED die. The pattern counting tool looks for grayscale pixel value patterns defined by features. Regardless of how pixel intensities vary between images, it can quickly and accurately find the patterns. Each time it runs, it can locate and identify thousands of die, even patterns as small as 4x4 pixels. The control system stores manufacturing history records and results tied to barcoded labels on finished packages.

Many modern 3D vision solutions offer simple and easy-to-use blob, volume, and edge detection tools to solve complex food inspection applications. The In-Sight 3D-L4000 smart camera, combined with Cognex In-Sight software, streamlines the process and makes these tools easy to use in a familiar spreadsheet format. The In-Sight 3D-L4000 offers the highest quality 2K resolution and a patented, no-spot blue laser optical configuration that generates more accurate 3D point clouds for inline measurements. Because the In-Sight 3D-L4000 can generate a complete 3D point cloud, rather than just a height map above the surface, it can more accurately apply 3D blob tools to identify different, but touching, objects, such as cookies in a container. The In-Sight 3D-L4000 also offers a variety of true 3D algorithms for volume measurements, such as the distance between cylinders.

Cognex Deep Learning is ideal for detecting small, unpredictable defects. It is trained on a set of chocolate images that includes the full range of acceptable variations. The defect detection tool then flags any chocolate that falls outside of this range, regardless of the type of defect. These must all be removed to maintain quality standards.

Cognex Deep Learning is ideal for sorting coffee beans because it can quickly and easily identify different colors, sizes, and characteristics of coffee beans. It can be trained on a set of images of each type of coffee bean that is received in the warehouse. Even if different types of beans look very similar, the classification tool can distinguish between different types, while accepting natural variations within each type. The defect detection tool can identify any physical contaminants in a batch of coffee beans before they are sent for blending.

Cognex AI image analysis software is as reliable as human inspectors for detecting surface appearance defects, but with the speed of a computerized system. The defect detection tool can create reliable shape and texture models based on training images, and can find defects on rough materials even with poor image quality under standard lighting. It then identifies surface texture changes as anomalies, and then uses the classification tool to classify them as collisions or scratches. Continuously providing high-quality products to the market can improve customer satisfaction and improve brand loyalty.

Cognex's In-Sight 7800 Series Visual Systems boast exceptional accuracy in reading tax stamps' OCR codes, helping tobacco industry manufacturers comply with stringent regulations concerning cigarette taxes. Leveraging the PatMax Object Location Tool, In-Sight 7905 searches for and positions patterns on fiscals stamps, followed by Exposure Distribution Graph and OCR tools to locate characters and decode OCR codes. Operating flawlessly at minimal distances, In-Sight 7905 extracts and examines high-resolution, high-contrast images unaffected by deformation directly from high-speed printers.

Employing Histogram Tools, In-Sight 7800 examines representative signs of poor packaging, including uneven brightness and absence of brightness whilst installed behind exterior linear lights. Such arrangements facilitate high resolution, swift processing, and broad fields of view vital for monitoring the rapid movement of cigarette rolling devices surrounding assembly wheels. An incorporated pan-tilt housing safeguards against debris originating from tobacco production machinery. Additionally, protected by IP67-rated lens covers, In-Sight 7800 ensures durable functionality. Configurable using intuitive and easy-to-deploy In-Sight Manager software, integration with factory automation environments remains straightforward.

Cognex In-Sight 2000 sensors guarantee correct cutting according to specified parameters, leveraging Edge Detection Tools to find either end of cigarette filters and pinpoint the center point for cigar cuttings. Boasting compact designs fitting snuggly into tight spaces available within tobacco equipment, In-Sight 2000 integrates potent built-in lighting delivering essential illumination coupled with multiple wide-angle camera options providing expansive views at close working distance ranges, capturing high-resolution images.

Cognex In-Sight 2000 sensors offer superior performance for dual inspections where space constraints necessitate diminutive dimensions - measuring just 92 x 60 x 52 mm. Powerful integrated lighting configurations deliver high-contrast, high-resolution images even at close proximity, suitable for checking filter paper alignments and dot glue detections. Upon discovery of defects, sensor transmits findings to programmable logic controllers (PLCs) promptly halting machinery operations, preventing resource squandering and blockages in tobacco production machines.

Cognex Deep Learning calculates the number of mouthpieces housed within large shipping cartons entirely, tackling this demanding application head-on. Merging positional instruments located overhead with million-pixel cameras and parallel placements of strong external linear lights, this approach counts transport containers holding over four thousand mouthpieces. Trained using a modest group of sample images, Cognex Deep Learning distinguishes among various mouthpiece defect categories, including white carbon, activated charcoal, concave structures, and others, ensuring accurate enumeration regardless of mouthpiece dimension, hue, or form.

Cognex AI-driven technologies identify and classify tears or holes appearing on cigarette boxes, assisting in the detection of potential production concerns threatening product quality.

Cognex 3D Laser Displacement Sensors deliver high-resolution three-dimensional imagery swiftly for every crystal in large trays, ensuring micrometer-level precision while detecting discrepancies in proper positioning. Upon identification, the measured information is transmitted back to programmable logic controllers (PLCs) or robots, adjusting and fine-tuning the grabbing mechanism for skewed or misaligned crystals.

1. Cognex Deep Learning software can perform multiple feature location and identification tasks on a single image. Cognex Deep Learning classifies and distinguishes the functional features of different chocolates based on size, shape, and surface features.
2. Users can train the location tool to locate each type of chocolate that needs to be found. Users can create a database of different types of chocolates for the location tool to locate, and then the location tool can be used to perform packaging verification.
3. After training, the image can be segmented into different regions, and the location tool will be able to detect the presence of chocolate in these regions and verify that the type of chocolate is correct. Multiple configurations can also be created for different packaging variations on a single production line. In this way, users can automate the chocolate packaging verification process with just one tool.

Machine vision solutions can measure the volume of food dispensed during product assembly to ensure food portioning. 3D displacement sensors provide highly accurate 3D volume measurements specifically designed for food portioning. 3D calibration allows manufacturers to set food portioning and cut points during processing to ensure that portions are uniform and consistent. Cognex 3D displacement sensors are factory calibrated to detect 3D food products. The Cognex 3D laser displacement sensor can scan the dimensions of individual portions to determine if the portion meets the pre-programmed specifications. Visual inspection and volume measurement can be performed after scanning.

Cognex Deep Learning's defect detection and classification tools are trained on a variety of qualified and defective weld joint variations, and learn to accurately classify and distinguish between functional and cosmetic flaws. By using an example-based approach rather than traditional rule-based machine vision, application development time can be reduced.

Using defect detection tools, engineers train the software in supervised mode with a collection of images labeled according to whether ceramic capacitors or electrolytic capacitors belong to the category "pass." During operation, the model captures and differentiates both kinds as belonging to the same type. Subsequently, the classification tool learns each unique capacitor property and accommodates intratype variation. Even if they look similar visually, color and label differences distinguish varying electrolytic capacitors effectively. Meanwhile, Cognex Deep Learning accurately classifies and distinguishes individual capacitors within singular images throughout runtime based on patterns learned during development.