The Fourth Industrial Revolution is ramping up. But what is it exactly? What technological advancements enable this new wave of transition to more advanced production means and processes? And what is the role of machine vision in this huge gear train? Let’s take a quick spin through the history of manufacturing to understand the context of transformation that currently occurs within factories, marked by a number of milestones introducing new means to mechanize production and push it to the next level.

The first breakthrough (the First Industrial Revolution) was the invention of the steam engine in the mid-18th century.

At the end of the 19th century, steam power started to be displaced by electric power. This Second Industrial Revolution enabled mass production through the invention of assembly lines.

The late 1900s saw the emergence of computers, electronics, and digital technology, which ignited the spread of automation. This Third Industrial Revolution, or “Digital Revolution,” enabled the automation of entire production processes through the deployment of computers, machines, and robots. And then human and machine capabilities started to merge. These “cyberphysical systems” marked the Fourth Industrial Revolution, or Industry 4.0, transforming traditional production facilities into smart factories, where everything is fully connected through a communication network for data exchange — between machines, humans, and facilities.

Within this interplay of technologies, machine vision plays a pivotal part. Let’s take a look at its impact on the manufacturing automation of the future and how it propels factory transformation.

Why a smart factory cannot be smart without machine vision

A smart factory is a highly digitized, fully automated, connected, and flexible manufacturing environment that relies on data and communication. It makes use of the most advanced technologies that enable the collection, communication, and analysis of data, including machine vision, artificial intelligence, and the industrial internet of things.

Machine vision plays a core part in data generation and collection: It captures the physical world and transforms it into digital data in the form of point clouds so that the data can be further evaluated and translated by AI algorithms into valuable information.

It also extends robotic capabilities to unprecedented levels. Robots equipped with 3D machine vision and intelligence can perform the most complex and sophisticated tasks within a factory. 3D vision helps robots navigate spaces and accomplish operations that require dexterity. It is critical for tasks such as real-time process control, product inspection and quality control, object handling and sorting, robot guidance, and predictive maintenance of machines. 3D data helps to detect issues such as defective machines and to facilitate fast intervention.

To enable these robotic tasks, machine vision needs to provide large amounts of high-quality real 3D data. This is necessary so AI algorithms can work with this data and process it into useful information, which is then further communicated throughout and outside of the factory to other technologies. These can analyze it and learn from it so decisions can be made accordingly.

Facilities that adopt machine vision to optimize manufacturing operations can see an exponential increase in productivity and efficiency. This leads to lower costs, better product quality, less waste, and the prevention of crises related to the shrinking labor force.

The market offers a number of different machine-vision technologies. So what criteria should be applied when selecting the right machine vision for a smart factory?

Machine-vision challenges and advancements

The development of machine-vision technologies has not reached the final stage. Developers of 3D-vision systems constantly improve their solutions to take vision-guided robotics one level further. Yet one challenge could not be solved with standard technologies, which tremendously limited the range of applications that could be automated within a factory.

This challenge resided in capturing scenes in motion and the seemingly “inherent” tradeoff between quality and speed.

Imagine products or product components that are placed on a moving conveyor belt. When they reach a robot equipped with a 3D-vision system, the vision system scans the parts, one by one. The output of this scan is a 3D point cloud with precise X, Y, and Z coordinates. This 3D data is used to navigate the robot to approach each part, pick it, and place it at another location or perform some further action with it. Or the 3D data can be used for inspection and quality control. These robotic tasks may seem rather simple, but they are not. In fact, they represent the most challenging applications for machine vision.

On the other hand, structured light systems offer submillimeter resolution and high accuracy, but those come at the cost of speed. In other words, structured light systems can deliver high-quality 3D data if the scanned object or the camera does not move.

The tradeoff between quality and speed limits vision-guided robotics and machine-vision applications to tasks that involve static scenes and fixed-vision systems. However, Parallel Structured Light, which enables 3D area scanning in motion while delivering high resolution and accuracy, can overcome this limitation. The technology was developed by Photoneo and enables the capture of moving scenes without motion artifacts.

The possibility of scanning dynamic scenes opens up countless applications that could not be automated before.

Among these are tasks that require hand-eye coordination — that is, mounting a 3D-vision system directly onto the robotic arm. Traditionally, the robot needed to stop moving to make a high-quality 3D scan. This is not necessary anymore, which significantly shortens cycle times and increases productivity and efficiency.

The resistance to the effects of movement or vibrations is a new machine-vision capability that starts a new era of manufacturing automation. Together with other advancements in the field, it helps transform traditional production facilities into the smart factories of tomorrow.

By: DocMemory
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