Cognex Corp. is an American manufacturer of machine vision systems, software and sensors used in automated manufacturing to inspect and identify parts, detect defects, verify product assembly, and guide assembly robots.
Major components of a machine vision system include lighting, lens, image sensor, vision processing, and communications. MI apps include GIGI, or guidance, identification, gauging, and inspection.
Here, Lalit Kumar Mishra, Regional Sales Head, India Subcontinent, Cognex, tells us more. Excerpts from an interview:
DQ: How are you laying the foundation—connectivity and a unified IT/OT digital platform—for Industry 4.0 use cases?
Lalit Kumar Mishra: Our business is about machine vision. At Cognex, we apply machine vision to some of the most complex in-line applications performed by the world’s most sophisticated manufacturers of discrete products.
Our technology is used to help customers in four general areas: guiding (to guide equipment, examples include aligning a screen on a smartphone, or guiding a robot to put a windshield on a car)., identifying (we read alphanumeric codes and barcodes in production.
We specialize in reading the most difficult-to-read barcodes at the highest speed and accuracy), gauging (we perform high-speed, in-line, precise, non-contact measurement in production). and inspecting (looking for defects, missing pieces, and irregularities on components and finished products).
Every company wants to get more production out of its lines and increase the number of different products they make on each line. There is an ever-increasing emphasis on quality. The cost of quality is being measured more actively, and vision is increasingly used to measure, understand and improve it.
We offer a set of vision devices with its patented software and algorithms. Our camera devices would be able to read defects at the micron level. The Data generated can be used for running business analytics and making more accurate decisions. Each device may come with its own range of communication options, such as Ethernet, Wi-Fi, and Bluetooth.
DQ: Elaborate on the role of edge intelligence in manufacturing.
Lalit Kumar Mishra: Most of the manufacturing organizations are looking to bring in edge intelligence in manufacturing where Cognex plays an important role. All our devices generate a lot of data which can then be consumed at the enterprise level. The Cognex Edge Intelligence (EI) platform transforms big data into intelligent data and provides real-time system performance monitoring and device management to improve overall equipment effectiveness (OEE) and throughput.
Machine vision tools and barcode reading systems produce a lot of insight-rich data across manufacturing and logistics facilities.Efficient data collection and analysis from highly interconnected devices forms the backbone of Industry 4.0 and Industrial Internet of Things (IIoT) initiatives. Edge computing supplements centralized cloud-based analytics by providing computational power right next to production lines and logistics operations to collect data and extract context-rich insights quickly
DQ: With so much on focus in semiconductor industry, what does Cognex have to offer?
Lalit Kumar Mishra: Electronics is a major focus area for us. Today, electronic components and devices cannot be manufactured without using machine vision. In fact, machine vision has made it possible to achieve the density in today’s integrated circuits and to manufacture them cost-effectively, electronic manufacturers rely on Cognex machine’s vision, deep learning, and 3D vision technology to build and inspect semiconductors, printed circuit boards, electronic hardware, and other consumer devices.
We see a huge market creation opportunity in India which is backed with government support. A lot of global giants are looking at making an entry in India whom we have been serving successfully for last several years in other regions. Their defacto standards on assembly lines has Cognex built into. The timing and opportunity in India seem to be for the next few years when we could help serve this fastest growing industry with the best practices we have already deployed with them outside India.
DQ: How are you reducing equipment downtime and minimizing human errors to decrease production expenses and improve quality and safety?
Lalit Kumar Mishra: For machine vision, the efficiency levels may vary from industry to industry, and customer experience may be different. However, concept of machine vision is undoubted especially when the parts are getting smaller and doing it manually is almost next to impossible. Vision-based system works best based on level of intelligence built in.
You may take down the manual rejection percentage from, say, 20, to 0.3. The barcodes that we alone can read might be behind shrink wrap on a pallet, damaged or occluded, or laser-etched codes on metal parts where reflection may be a challenge.We have a higher read rate of barcodes in challenging applications than any other company in the world
Deep learning technology is used to predict patterns and perform judgment-based applications. This technology deploys AI algorithms to teach robots and machines to do what comes naturally to humans. Cognex deep learning is designed for factory automation. Its field-tested algorithms are optimized specifically for machine vision.
Combining artificial intelligence (AI) with In-Sight or VisionPro software, it automates and scales complex part location, assembly verification, defect detection, classification, and character reading inspection applications that, until now, were too challenging for traditional machine vision alone. The system works on the repository of images, the more the images and feed, the better the output of the system be.
DQ: What is Cognex doing to impart training?
Lalit Kumar Mishra: We work with customers to organize Tech Days. We demonstrate and talk about what vision systems can do in terms of applicability across industries. Various functions attend these Tech Days, try and understand the apps and come up with requirements where vision automation can be applied. It creates a learning environment for customers.
We are also coming up with certification process where in we are looking at launching two levels. Level 1 can be for easy to setup products and Level 2 is going to be for advanced apps. Level 1 can be barcode, fixed lines, handheld scanners, etc. Level 2 can be deep learning, 3D, etc. This may be launched by second quarter of 2022.
DQ: Give at least two examples of any recent Industry 4.0 deployments.
Lalit Kumar Mishra: We have several examples.
A leading tyre manufacturer has used Cognex for developing a tyre identification kit. The kit checks for the height and width of the tyres etc. The company has gone for multiple repeat orders. 3D Pallet Tyre Counting with deep learning SKU Mismatch is another project with the same customer. A 3D camera is used from top to count total number of tyres in pallet. Deep learning is used with two color cameras from sides to identify SKU mismatch by thread pattern and color ID line.
A large automobile company deployed our solution to track-and-trace in-house manufacturing components on each machine. They used scanning of 2D Datamatrix DPM laser mark code reading on each station. Post scanning, data is sent to the enterprise system, which is then consumed on their IoT platform. They check for scanning stages and the workflow designed wont allow to move to next step if any component misses even a single stage.
DQ: How has Cognex been doing in India?
Lalit Kumar Mishra: In India, Cognex is a sales and marketing focused organization. Defect rates across our products are the lowest in the industry. We work with selected set of partners across the country who provide implementation and post-sales support services to our end customers. We have a very high focus on product quality and R&D.
We primarily work with end customers who are manufacturing organizations of repute, and also with large machine makers who are producing machines for some of the global leaders in manufacturing, worldwide. The machine making market in India is all set to grow with a lot of focus on exports.