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  1. Color Inspection comes to the ViDi Platform Cognex has announced the release of Color cameras for use with its ViDi Deep Learning platform. If you haven't been keeping up with Deep Learning in Machine Vision, this is huge news. Not very long ago Deep Learning and neural networks were limited to lab use or very large complex systems. But now you can train complex applications and deploy them on Smart Cameras on your product line to perform inspections that were never possible with traditional rules based machine vision. Read the blog here at Cognex "5 Inspections Made Possible with Color Imaging and Deep Learning" By utilizing the D900 platform, Cognex is bringing Deep Learning out onto the production floor in unprecedented ways. Whether it's Kitting, Defect Detection, OCR or other difficult inspection tasks, Cognex has a system that can tackle your toughest applications. Benefits of the D900 ViDi System: 2.3MP and 5MP Grayscale or Color Cameras with HDR+ IP67 Rated Housing for Ruggedness in the Industrial Environment Performs inspections not possible with traditional Rule Based machine vision No PC on your production floor Familiar In-Sight Spreadsheet programming environment Ease of Deployment
  2. Expiration dates, lot codes, and other important texts are on all our consumer products. Federal regulations require that food and medical related items have this important information and that it is easily read by the end customer. Consequently, manufacturers are responsible for making sure these texts are present and accurate. Traditional optical character recognition (OCR) tool sets use a combination of image filters and pattern matching to determine which character is being read. A large set of tool parameters can be adjusted to help decide if what the camera sees is a character or just a mess of like colored pixels. Below you can see how an application using a traditional OCR tool works great when the text is clearly printed and very consistent. What happens when the surface for the printing is uneven (color, texture, reflectivity) or perspective distortion causes the shape and/or proportions of the character to fall outside the nominal range set in the parameters? The toolset struggles to properly segment the characters and is confused as to what some of them are. The badly scratched/smudged ‘R’ is even being found by the OCR tool as two separate characters. Enter Deep Learning OCR tools. ViDiRead from Cognex leverages deep learning algorithms to decipher badly deformed, skewed, and poorly etched codes using OCR. The In-Sight ViDiRead tool works right out of the box thanks to pre-trained font libraries which dramatically reduce development time. Simply define the region of interest (ROI) and set the character size. In situations where new characters are introduced, you simply capture a handful of images, label the unknown character, and click train. Using the same first image from before, we can train the ViDiRead tool and it reads as expected. A closeup of the last two characters shows nicely formed characters that the tool has no issues decoding. Now when we use the image of the damaged label, the ViDiRead tool has absolutely no problem reading the characters. Even though the ‘R’ is poorly printed due to scratching/smudging, ViDiRead does not have any issues reading it. Traditional OCR tools are great in many applications but there are some extra difficult-to-read texts that just do not allow these tools to be used. When all others have failed, ViDiRead will succeed.
  3. Version 1.0.0

    0 downloads

    We have been encountering the buzzwords "AI", "Deep Learning", etc.. Now it's reality with Cognex's new Insight D900 system with Deep Learning Technology. Check out this video to reveal why we call it revolutionary!
  4. Version 1.0.0

    0 downloads

    Did you think that you had to remove your part with a barcode out of your automation line to verify it? Or did they tell you it's impossible to verify on a super fast speeds? With the DataMan 475V InLine Barcode Verifier, you don't have to sacrifice speed on your automation line or go through the added expense of creating an online verification station.
  5. Version 1.0.0

    0 downloads

    Want to learn the steps on how to set up a vision-guided robot application? Attend this powerful session to learn how to use the RT Toolbox3 software from Mitsubishi and Cognex's InSight Explorer software to easily simulate a vision-guided robot project. Perfect for validating an application's proof of concept without hardware or while working remotely! Learn the steps to make this connection, see how easy these projects are to simulate, and walk away with sample code & a project that you can start using today!
  6. Version 1.0.0

    0 downloads

    Please join us for a short technical webinar to learn about the different 3D technologies available for machine vision and what applications they're best suited for. See first-hand the benefits of the different technologies utilized in Cogenx's 3D inspection portfolio, complete with application-specific examples and live demonstrations!
  7. Cutting-edge performance for the toughest codes Manufacturing environments in automotive, medical device, electronics, aerospace, and product security industries require rugged, high performing, and easy to use barcode reading technology to ensure top component traceability and productivity. DataMan 8700 series handheld barcode readers decode the toughest direct part mark (DPM) and label-based codes while withstanding harsh oils, dirt, and water. A built-in display screen enables quick setup and operator feedback, and the readers support a broad range of industrial protocols and communications to connect and operate efficiently in any facility. Robust oil and water resistant housing In the past, oils and fluids surrounding an application site have quickly damaged barcode scanners, but the DataMan 8700 barcode reader is built to last in the harshest environments. It has IP67-rated housing and meets the ISO 16750-5 oil specification for oil resistance. It is submergible in one meter of water without causing damage and withstands multiple drops from 2.5 meters onto concrete. Premium read performance in milliseconds The DataMan 8700DX model is equipped with the latest patented decoding algorithms, HDR technology, high speed liquid lens, and a multi-core processor to read codes in under 150 milliseconds. Integrated diffuse, polarized, and direct lighting enables superior image formation to read dot peen, laser-etched, and label-based codes on challenging (shiny, cylindrical, dark) surfaces without any hot spots. OLED display screen makes setup easy DataMan 8700 series provides a first-of-its-kind OLED display screen to make setup easy and to give operators quick and intuitive feedback without connecting to a PC. Setup the reader using the configurator buttons below the screen Name the reader to know which station it belongs to View the wireless signal strength to ensure the reader is within range Monitor battery life so the reader doesn’t power down mid-shift Review code data to ensure a successful read Supports more communication options Integrated industrial protocols enable DataMan 8700 barcode readers to easily connect to PLCs and factory networks. DataMan 8700 supports more communication options than ever before including Wi-Fi 5 GHz and 2.4 GHz, Bluetooth 4.2, and a corded option. It allows for a direct connection to computers, tablets, and phones.
  8. Version 1.0.0

    1 download

    We have been encountering the buzzwords "AI", "Deep Learning", etc.. Now it's reality with Cognex's new Insight D900 system with Deep Learning Technology. Check out this video to reveal why we call it revolutionary!
  9. In the 2D industrial imaging realm, I am going to split the cameras into 2 groups: Monochrome and Color cameras. They both have different applications and we usually see monochrome cameras used in the automation industry and machine vision applications. Take a look at the bottom infographic for how to turn light into a digital file. Industrial cameras are digital cameras so this applies to them too. Light hits the subject (no light, no image), a lens collects the light then gets reflected on a sensor that gets converted into signals. Let’s dive into the different parts of this process. Lensing Why do you need a lens to get an image? It is a given that our cell phone cameras have lenses on them, some of our industrial cameras come with autofocus lenses but do we really need a lens to get an image? Actually, you don’t. You can get an image without a lens, it just might not look great. If you have an interchangeable lens DSLR sitting around or if you have a removable lens industrial camera laying around, take off the lens (please be careful with your image sensor…). Take a needle, and make a hole in one of your business cards. Put the business card where the lens might go, and you are going to find that you can actually get an image this way. Crazy! So the job of a lens is to collect light and redirect it to the image sensor. You can accomplish the same function with a pinhole(called a pinhole camera), it’s just not very effective. Image Sensor/Imager Image sensor is where most of the magic happens. Manufacturers mostly use two different image sensors: 1) CMOS (Complementary metal–oxide–semiconductor) 2) CCD (Charged coupled device). I am not going to dive into these technologies but CMOS is what is being used mostly because of the cost and speed of the technology grabbing information from the world. Briefly, on a CMOS sensor, there are little “sensors”(called transistors) that reflect a voltage value depending on the amount of light they receive. On a monochrome camera the light that got collected from the lens hits each pixel on the sensor, the transistor behind reflects a voltage value depending on the brightness, you end up with various voltage levels that represent a grayscale image. Color Filters and Color Cameras Mainly, there are 2 types of hardware technologies manufacturers use to get color images from their image sensors. The main component is the filter in front of the sensor. The phenomenon that you need to understand here is that, when you look through a red film, you will be seeing red. Conversely, if you were a monochrome camera, looking through a red film, the red lights would show up as white. The first and expensive way of getting a color image is to have 3 different image sensors, one to capture Red, one to capture Green and one for Blue. So then, in software, you can create that RGB image. Gets very complicated with how to interact between 3 different imagers, triple the cost because of the hardware, bigger cameras to fit all the sensors and circuitry. Second way of getting a color image is to implement a Bayer filter in front of the image sensor. You can see an image of a Bayer filter in the picture below; It is important to spot that there are way more green pixels than blue and red (%50 green, %25 blue and %25 red to be exact). This is because human eyes like to see green. There is not a lot of reason other than it looks good to the human eye. And if you have been working with industrial cameras for some time now, what looks good to the human eye might not actually be the best image for inspection purposes. Another disadvantage is that you can basically think that you are losing 1/3rd of the resolution of your image. In machine vision, specifically in measuring application, we see the resolution of the camera being very important to hit the right tolerances. So you will find yourself paying for a more expensive color camera with more resolution to get the same tolerances as a monochrome camera. To sum up, it is possible to summarize the image acquisition process in terms of hardware into a couple of steps. Light hits the subject, a lens collects the light, depending on the type of sensor or the filter on the sensor, light hits the pixels and we convert the light into electrical signals. Rest is software. Image Resources :https://en.wikipedia.org/wiki/Bayer_filter https://www.loveyourlens.co.uk/which_entry_level_digital_camera/digital-camera-sensor/ https://meroli.web.cern.ch/lecture_cmos_vs_ccd_pixel_sensor.html
  10. Download and install the the version of InSight Explorer that matches the firmware on your camera. https://support.cognex.com/en/downloads/in-sight/software-firmware If you are unsure which version you need, get the latest and greatest, then you can use it to determine what firmware you are running and therefore what version of software you should be running as the two should always match. After installing InSight Explorer, you will need to get your Emulator License: Open InSight Explorer Using the pull-down menu at the top: -> System -> Options From the dialog box, choose “Emulation” Copy “Offline Programming Reference” to the clipboard Go To http://www.cognex.com At the top right create an account, or if you already have one, log in. Open this web page - https://support.cognex.com/en/InsightEmulatorKey Enter a value for "Company Name" and paste your Offline Programming Reference key into the entry field. Click “Get Key” Copy the provided “Offline Programming Key” Paste this key back in your InSight Explorer dialog box for “Offline Programming Key” Click Apply Click Okay Restart InSight Explorer You should be all set!
  11. For most of us in the industrial automation field, seeing a vision solution on a machine is commonplace. Some of us have specified them, some of us have programmed them, and some of us only know, “that’s the vision system over there.” With the latter being the most common, it becomes difficult for some to decide between the types of vision solutions. There are typically two types of self-contained (no separate controllers required) options: vision sensors and vision systems. There is a third type, PC based vision systems, that is an entirely different animal and you will know when you need it. So why would you want a smart sensor in your application? Smart sensors give great ‘bang for the buck’. They carry a good set of features comparable to low end smart cameras at a fraction of the cost. Since these sensors do not require the computational horsepower or high-end feature sets of smart cameras, they can use smaller components with less heat dissipation requirements. This allows them to be packaged in much smaller housings that take up less space in a machine and reduces the overall weight of the sensor to reduce robotic payload burdens if mounted to end of arm tooling. If all you are looking for is to know the absence or presence of a feature pattern, edge, simple OCR/OCV, blobs, etc then a smart sensor is a great choice. A key differentiator from sensors to systems is that the sensors are generally much easier to program. Graphical programming environments with slider bars that immediately update the results of an inspection help set up a sensor very quickly. If you need more horsepower because of inspection speed requirements, more internal logic capabilities, higher resolutions (2Mp and up), dimensional measurements, advanced OCR/OCV, 1D/2D code reading, defect detection, etc, then you will need to look to a vision system to solve your application. There are a lot of different models that will solve a given application with a price and feature set to match what you need. These vision systems can be programmed to solve just about any vision task thrown at them. The newest systems leverage machine A.I. and deep learning to solve applications previously only able to be completed by humans. These full vision systems are typically configured by an integrator or distribution partner who has been trained in setting up vision systems which adds to the cost of the entire system. With all the differences, obvious or subtle, between the two types of solutions, there are a bunch of features to note that are common to both when talking about Cognex as a vision solution manufacturer. Programming software (except A.I. and deep learning versions) is the same for both the sensors and systems. Both are available with color imagers, autofocus lenses, integrated lighting/lens filter choices (white, colored, UV, and IR). So, whether your application is simple or complex, high speed or slow, or on a tight budget, there is a solution for you. Knowing which is the right solution might just take a closer look.
  12. Let’s say you are trying to implement a pick and place application with your robot. Industrial robots are amazing in terms of going to the place they were told to go. But what if that place we told them to go changes constantly and we don’t know where the part is going to be next time around. That’s when we use machine vision’s help to guide our robot to the right pick location. The general idea is that a vision system needs to be looking at the potential pick locations, and tell the robot where to go and pick up the next part. I’m sure a lot of you would agree that communication is the key to success. That is no different in this case. If two people are speaking different languages that conversation is not going to work great. In digital cameras, there is a sensor that collects the light from the outside world and converts it into electricity. The sensor has “points” (or you can call it a grid) on it that are called pixels. The images we obtain from the camera are represented in these pixels. Robots on the other hand, have coordinate systems. And they usually get represented in meters or millimeters. Two different languages... The whole magic is to be able to know where the camera is located relative to the robot end of arm tooling. Camera can either be mounted at: The end of arm tooling A stationary location If the camera is mounted at the end of arm, we need to know the location of the camera relative to our gripper. At this stage, we only need to know the relative location in terms of two dimensions. The third dimension, we usually can control. For example, if the camera is mounted 3 inches in X and 1.5 inches in Y away from the end of arm tooling, and our part is in the middle of camera’s field of view, the robot needs to move -3 inches in X and -1.5 inches in Y (on its end of arm tooling’s coordinate system) to be able to grab the part. Wait a second, how about the Z? In the robot program, I always have a set location to take my picture before the pick so I know how far the robot end of arm tool is relative to my parts in terms of Z. But what if the part is not in the center of the field of view of the camera, the camera needs to report the part’s location somehow to the robot right? Yes, and that’s when the calibration comes into play. Basic idea is to calibrate the camera’s pixel readings into robot coordinates. Since most of the time, the robots work in real world coordinates (mm or m), you can use the built in calibration function on your camera software if it has one. These routines usually require some type of a grid with known size squares or circles so the camera can do math to convert it’s pixels to real world coordinates. I usually don’t do that. I would have to make sure my axes are aligned perfectly and would have to take lens distortion into consideration somehow. I make a randomly marked paper or plate (see the plate below with holes in it) and use that as the calibration grid. Here are some simple steps and tips to get the calibration process working; Jog the robot to the location where the picture is going to be taken and take a picture. Make sure all the markers on the plate are visible on the image and the top of the plate is the same distance away from the camera as the top of the part you are eventually going to pick up. Note all the locations of the markers in pixels from the camera software. You will need to implement some tools in the vision system to be able to get location data from these markers. Jog the robot to each of these markers and note down the locations in robot coordinates. All you need to do now is to match the pixel location of the marker and the robot location of the marker and do math! Cognex Insight cameras have a built vision tool called N-Point calibration to do this very easily. You select the markers on the plate and write down the corresponding robot coordinate in a table. The software takes care of the rest and your location tools (like PatMax) will report back in robot coordinates now. Almost the same process when the camera is mounted on a stationary location. You just need to know where the camera is located relative to the arm. Only other thing to be careful about now is that the camera needs to be farther away from the pick area so that the robot arm can swing in and grab the part without hitting the camera. Understanding the basics, it’s not so scary to do a vision guided pick and place anymore. Does it still scare you? If so leave a comment below or reach out to us at https://www.gibsonengineering.com/. Disclaimer: This blog post is valid for using 2D cameras for doing a pick and place of the same type of part. Depending on the parts and the hardware used, some additional steps might need to be taken.
  13. Version 1.1.0

    1 download

    The attached file describes the setup process of connecting a Cognex Dataman barcode scanner with Ethernet to a Mitsubishi FX5 (iQ-F) series PLC over ethernet using the SLMP protocol.
  14. Version 1.0.0

    4 downloads

    The attached file shows how to get data into KepserverEX via Modbus/TCP from a Cognex Dataman barcode scanner.
  15. Version 1.0.0

    0 downloads

    The attached file shows how to connect a Mitsubishi Robot to a Cognex In-Sight Vision System in order to do a Tracking application.
  16. Version 1.0.0

    3 downloads

    The attached file shows how to connect a Mitsubishi iQ-F (FX5) series PLC to a Cognex In-Sight Vision System via MC Protocol Scanner.
  17. Version 2.0.0

    5 downloads

    The attached file shows how to connect a Mitsubishi iQ-R series PLC to a Cognex In-Sight Vision System via MC Protocol Scanner. Similar methodology can be used for the iQ-F series, L series and Q series PLCs and with slight modifications also the FX3 series PLCs.
  18. Version 2.0.0

    0 downloads

    The attached file shows how to connect a Mitsubishi iQ-R or iQ-F series PLC to a Cognex In-Sight Vision System via MC Protocol Scanner. Similar methodology can be used for the iQ-F series, L series and Q series PLCs and with slight modifications also the FX3 series PLCs. This includes libraries for FX5 and iQ-R for CameraStatus and CameraControl blocks.
  19. This Process Control Metrics (PCM) video provides an overview of the PCM in the Cognex DataMan industrial code readers. It explains the difference between “verification” and “process control metrics”. This educational video talks in detail about Process Control Metrics which allow you to monitor the quality of the codes being read and identify key aspects of the print quality while online to ensure downstream readability. The PCM Application makes it easy to configure and report the PCM results. From this video you will understand how PCM relates to: Key quality metric aspects Detecting changes in code quality Preventing issue before read rates decline Ensures downstream readability
  20. Designed for high-speed, high-throughput manufacturing and logistics lines, DataMan 470 is the fastest and most powerful reader in the company’s line of industrial barcode readers. DataMan 470 uses new, patent-pending imaging technology to easily solve challenging applications. The High Dynamic Range Plus (HDR+) technology enhances the image quality of 1D and 2D codes, in addition to providing greater coverage and faster speeds than conventional readers. It also reads low contrast and ultra-small codes not visible to other readers, and covers larger inspection areas with fewer cameras, allowing greater process variation and lower facility design costs. DataMan 470’s expandable technology platform includes multi-core processing power, advanced decoding algorithms, and modular configuration options to significantly improve speed and flexibility. DataMan 470 Barcode Reader
  21. Barcode verification is the process of grading the quality of barcodes. Barcode verifiers capture images and generate reports to demonstrate compliance to parameters within industry standardization guidelines. The DataMan 8070 series barcode verifier from Cognex is packed with powerful lighting options, robust grading algorithms, a high-speed processing engine, and a high-resolution camera to capture and grade the most difficult direct part mark (DPM) codes. Combined with simple DataMan Setup Tool software, the DataMan 8070 series verifiers deliver detailed, repeatable results. Multiple Options for Multiple Surfaces DataMan 8070 verifier is the only verifier with all three, 30/45/90-degree, lighting angle options specified in the International Organization for Standardization (ISO) AIM DPM Quality Guideline (ISO/IEC TR 29158). It easily illuminates codes on textured, curved, and even highly reflective surfaces, reliably capturing and grading code images to demonstrate code quality compliance. Fast, Accurate Algorithms for Reliable, Repeatable Results High-speed location and decoding capabilities combined with robust grading algorithms provide fast, accurate, and repeatable results. Detailed reports show whether codes meet industry standards and can be used to demonstrate compliance, as well as pinpoint printing and process control issues. Compact Design with Rugged Housing for Greater Mobility The DataMan 8070 series verifier has an ergonomic, handheld scanner-like design that is easy to maneuver. A small imaging head gives users better access to codes in recessed or hard-to-reach locations. The DataMan 8070 verifier is the only verifier on the market with an IP65 rating and rugged housing to withstand even the harshest factory floor environments. Download Cognex’s Barcode Verification Whitepaper
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