Understanding the Role of Machine Vision in Industry 4.0

Industry 4.0 is revolutionizing manufacturing as we know it. Built upon technologies integrating robotics, AI, machine learning, big data analysis, cloud computing, and sensors, this fourth industrial revolution is improving plant efficiency, increasing production stability, and minimizing operation costs. Manufacturers have reported 10-12 percent gains in areas such as output, factory utilization, and labor productivity after they invested in Industry 4.0 initiatives. Along the way, Industry 4.0 is delivering the societal benefits of enhancing sustainability and reducing pollution.


Industry 4.0 analyses data collected by smart sensors installed to predict outcomes and determine actions. In this eco-system, machine vision camera act as yet another sensor in collecting visual information about the physical world, much like the sensors that capture temperature, vibration, pressure, or flow speeds. Being digital, data from machine vision systems can be easily networked and shared with other sub-systems and devices throughout the plant in a cycle of continuous improvement.



To better understand machine vision, let’s look at an example of how it works. In this case, we highlight its use in the automatized detection of defective products — its most common industrial application. The process starts when a sensor detects the presence of an object on a production line, triggering a light source that brightly illuminates the area. A camera captures an image of the illuminated product at a speed measured in frames per second or “fps.” In most cases, a digitizing device called a frame grabber translates the image into digital output that is then transmitted and saved on a host PC. Specialized software on the PC compares the image against a set of predetermined criteria to identify defects. If a defect is identified, the product will fail inspection, and it will be physically removed from the assembly line. Vision systems like this can check for defects in the position of the product, its color, size, or shape, or it can determine the presence or absence of the object itself in its field of view.



Fulfillment of the error detection process requires orderly positioning of system components for the flow of information, starting with the sensor to the final processing of the image, as described above. In addition to the camera, illumination, host PC, frame grabber, and software, a machine vision system requires a lens, Ethernet, fiber optic, or Coaxial cabling, and various interface peripherals. While Ethernet-based "smart" cameras are used extensively on the edge, Industry 4.0 typically demands higher imaging speeds and resolutions than smart cameras can supply. For this reason, the CoaXPress (CXP) point‐to‐point communication standard for transmitting high bandwidth data over a coaxial cable has become the de facto standard for demanding machine vision applications. CXP carries low-latency, low-jitter images, signals, and power (Power over CXP) to the camera at up to 50 Gbts over a single cable.



In the age of Industry 4.0, machine vision is expanding beyond its traditional value-adding function of error detection. Today it is being applied to diverse areas such as monitoring processes for predictive maintenance, and robotic guidance that makes it possible for robots to safely work with and respond to human interactions.


When combined with artificial intelligence (AI), machine vision’s uses are virtually unlimited in solving manufacturing problems. For instance, AI can empower a machine vision system with self-adjustment capabilities so it learns from every cycle it performs in a feedback loop, growing smarter and smarter at each turn. Machine learning can make vision systems highly proficient at making sense of large image datasets far beyond the abilities of a human. The idea of adding a self-learning algorithm to machine vision is also intriguing because vision systems traditionally work with a fixed set of rules, making them inflexible when confronted with the need for fast changes. This is important as modern production lines are designed as flexibly as possible for quick adaption to small batches of custom products, a cornerstone of Industry 4.0.


Another technology assisting machine vision's adoption in Industry 4.0 is embedded computing. Essentially, it performs analysis at the source of the data or "on the edge," rather than transmitting data over an already crowded network to servers at a secondary location, therefore reducing bandwidth requirements. At BitFlow, for instance, we have combined our Claxon CXP 2.0 frame grabber with the NVIDIA® Jetson AGX Xavier Developer Kit, achieving a very small form factor image processing system ideal for edge computing.


Besides manufacturing, one exciting area for Industry 4.0 is the guidance systems that permit robots and co-bots greater autonomy and pathfinding abilities. Besides helping robots work faster and more safely alongside human workers, machine vision empowers robotic order pickers to significantly improve response time and limit fulfillment defects. Cameras can also be used to collect SKU data that enhance visibility across the enterprise, such as spotting recurring patterns that can predict possible shortages, the root causes of equipment failures, or other warehousing anomalies. Warehouse systems become smarter, faster, and more efficient in providing precisely what customers need by using machine vision.


Machine vision plays a dynamic role in Industry 4.0 strategy, allowing networks, robots, and plant-level managers to visualize the manufacturing process through the extraction, processing, and analysis of real-time digitalized images. Vision is one of the most valuable senses in humans, and increasingly, in machines. A machine vision system can be implemented at almost every stage of Industry 4.0 and serve as hubs to generate rich data that gives managers visibility into operations.


Learn more at www.bitflow.com.


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