Machine Vision is Key to Industry 4.0 and IoT – ReadWrite

Machine vision joins machine learning in a set of tools that gives consumer- and commercial-level hardware unprecedented abilities to observe and interpret their environment. In an industrial setting, these technologies, plus automation and higher-speed networking, add up to a new industrial revolution Industry 4.0. They also offer brand-new ways to conduct low-waste, high-efficiency industrial activities.

Machine vision affects manufacturing, drilling, and mining. Further benefits are found in freight and supply chain management, quality assurance, material handling, security, and a variety of other processes and verticals.

Machine vision is going to be everywhere before long, adding a critical layer of intelligence to the Internet of Things buildouts in the industrial world. Heres a look at how companies are already putting it to work.

Machine vision is a set of technologies that gives machines greater awareness of their surroundings. It facilitates higher-order image recognition and decision-making based on that awareness.

To take advantage of machine vision, a piece of industrial equipment uses high-fidelity cameras to capture digital images of the environment, or a workpiece. The images can be taken in an automated guided vehicle (AGV) or a robotic inspection station. From there, machine vision uses extremely sophisticated pattern recognition algorithms to make a judgment about its position, identity, or condition.

Several lighting sources are common in machine vision applications, including LEDs, quartz halogen, metal halide, xenon, and traditional fluorescent lighting. If part of a barcode or workpiece is shadowed, the reading might deliver an error when there isnt one, or vice versa.

Machine vision combines sophisticated hardware and software to allow machines to observe and react to outside stimuli in new and beneficial ways.

The proliferation of Industrial Internet of Things (IIoT) devices marks an important moment in technological advancement. IIoT gives businesses unprecedented visibility of their operations from top to bottom. Networked sensors and cloud-based enterprise and resource planning hubs provide two-way data mobility between local and remote assets, as well as business partners.

The two-way mobility can be something as small as a mechanical piston or bearing. It can also be as large as a fleet of trucks, can yield valuable operational data with the right IoT hardware and software. Businesses can have their eyes everywhere, even when theyre strapped for resources or labor.

Where does machine vision fit into all this? Machine vision makes existing IoT assets even more powerful and better able to deliver value and efficiency. We can expect it to create some brand-new opportunities.

Machine vision makes sensors throughout the IoT even more powerful and useful. Instead of providing raw data, sensors deliver a level of interpretation and abstraction that can be used in decision-making or further automation.

Machine vision may help reduce the bandwidth requirements of large-scale IoT buildouts. Compared with capturing images and data at the source and sending it to servers for analysis, machine vision typically performs its research at the source of the data. Modern industry generates millions of data points, but a great deal of it can yield actionable insights without requiring transmission to a secondary location, thanks to machine vision and edge computing.

Machine vision complements IoT automation technologies extremely well. Robotic inspection stations can work more quickly and accurately than human QA employees, and they immediately surface relevant data for decision-makers when defects and exceptions are detected.

Guidance systems built with machine vision give robots and cobots greater autonomy and pathfinding abilities, and help them work faster and more safely alongside human workers. In warehouses and other settings with a high risk of error, machine vision helps robotic order pickers improve response time and limit fulfillment defects that result in lost business.

Todays and tomorrows economy requires companies and industries that operate while wasting far less time, material, and labor. Machine vision will continue to make drones, material handling equipment, unmanned vehicles and pallet trucks, manufacturing lines, and inspection stations better able to exchange detailed and valuable data with the rest of the network.

In a factory setting, it means machines and people working in better harmony with fewer bottlenecks, overruns, and other disruptions.

When you think about each of the steps involved in a typical industrial process, its not hard to see each point where machine vision can improve operations.

To manufacture a single automotive part, humans and machines collaborate to source raw materials, appraise their quality, transport them to a plant for processing, and move the items through the facility at each manufacturing stage. Ultimately, they see it successfully through the QA process and then out the door again, where at least one last leg of its journey awaits. At some later time, the retailer or end-user receives it.

Whether this product is at rest, in transit, or not even assembled yet, machine vision provides a way to automate the handling of it. It improves efficiency in every department, such as assembly, and maintains higher and more consistent quality levels.

Some applications are as simple as placing a line on a warehouse floor for an unmanned vehicle to follow safely. Other machine vision tools are even more sophisticated, although even the simplest examples can be game-changers.

Some of the most exciting examples of machine vision in the industrial world involve tasks once thought difficult or impossible to outsource to robots. As mentioned, picking from bins in warehouses is a process thats inherently risky when it comes to errors. Mistakes in fulfillment cost goodwill and customers.

There are already nearly 100% autonomous order-picking robots available today, which can navigate safely, inspect parts and products in the bin, make the right pick using a manipulator arm, and transport the pick to a staging or packaging area.

Ultimately, this means companies are at a far lesser risk of shipping damaged goods or incorrect SKUs that look similar to, but dont quite match, the one the customer ordered.

In some modern manufacturing settings, it can help employers automate and improve results from the QA process, even without sacrificing human jobs. Instead, automated inspection stations tackle this high-priority work while employees learn more cognitively demanding skills.

Cobots will likely achieve a 34% share of all robotics sales by 2025. This is due in large part to improvements in machine vision and the drive to eliminate as much inefficiency, inaccuracy, and waste from the modern industry as possible.

Expect machine vision to continue to evolve in the coming years and contribute further to Industry 4.0, which many call the Fourth Industrial Revolution. Eyes are already trained on newer, lower-cost products featuring embedded and board-level image processing with machine vision capabilities.

Machine vision capabilities will lead to even more widespread adoption of the IoT and machine vision and new ways for businesses to capitalize on digital intelligence.

Featured Image Credit: HAHN Group, CC BY-SA

Megan Ray Nichols is a freelance technical writer and blogger. She enjoys writing easy to understand science and technology articles on her blog, Schooled By Science. When she isn't writing, Megan enjoys watching movies and hiking with friends.

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Machine Vision is Key to Industry 4.0 and IoT - ReadWrite