Industry 4.0, also referred to as the Industrial Internet of Things (IIoT), is the fourth industrial revolution. It encompasses cyber-physical systems, automation, and the Internet of Things (IoT) to create a better-connected manufacturing ecosystem known as a smart factory. A smart factory represents a leap forward from traditional automation to a fully connected and flexible system. Manufacturers are implementing smart factories for manufacturing operations, quality inspection, inventory tracking, warehouse operations, and equipment maintenance. This requires the deployment of various physical and digital technologies such as robotics, artificial intelligence (AI), additive manufacturing, high-performance computing, cognitive technologies, and augmented reality to connect equipment and facilities, and analyze the data connection and digitize operations.
One of the most important features of a smart factory is implementing industrial robots and robotic processes. This implementation makes the facility autonomous, where systems can execute decisions with minimal or no manual intervention. Industrial robots are valued in the manufacturing industry because of several factors, including reliability, precision, predictability, repeatability, and imperviousness to hazardous environments. Deployment of robots and automated systems in manufacturing facilities supports mass production. Since mass production is becoming a norm, it has become mandatory to manufacture components that can properly fit into any corresponding mating parts. By ensuring all parts have been measured according to specific dimensions and quality standards, automated parts inspection facilitates mass manufacturing of these components at full speed and without delay.
Further, the admission of robotic processes helps reduce manufacturing costs. Mass production has already helped manufacturers achieve unprecedented economies of scale. With the installation of automated systems, labor cost is minimized, and manufacturing materials wastage is also reduced, thus cutting down overall manufacturing costs.
The adoption of industrial robots and automation systems has grown manifolds over the past few years. According to the World Robotics Report 2021, approximately three million industrial robots are operating in factories worldwide, an increase of 10% compared to 2020. This growth was followed by positive market developments in China, compensating for the contractions of other markets.
As per the World Robotics Report, 2021, Asia remained the largest base for the deployment of new industrial robots, with nearly 71% installation. China remained the world´s largest industrial robot market with a share of 36% of total installations and about 154,000 units installed, followed by Japan and the U.S.
Machine vision plays a critical part in directing the operations and optimizing the productivity of robots and other equipment in the factories. Unlike simple sensors, vision sensors generate large amounts of image data, making them an important element in the Industry 4.0 environment.
Machine vision systems are paired with industrial robots to enable automation systems. Machine vision can be divided into two types: 2D and 3D. 3D systems generate more informative data in all three dimensions, making them ideal for complex robotic tasks that need to cope with diverse object shapes and orientations. When properly deployed, 3D vision systems can perform repeatable tasks with high accuracy and avoid errors due to object location, orientation, and presentation to the sensor. 3D vision systems excel at handling the intricacies of three-dimensional workpieces and are ideal for less organized applications in nature and involve a random presentation of parts.
Traditional industrial robots are designed to perform simple tasks, whereas vision-guided robots with an advanced vision system can perform critical tasks with variation and flexibility. The advancement in machine vision technology has enabled vision sensors to play a vital role in the 3D robot positioning systems. An embedded vision sensor robot has a greater consciousness of its environment. Its vision helps it hold and place objects in bins, racks, or pallets. Regardless of the position, a vision-guided robot (VGR) can pinpoint an object for further processing.
Vision-guided robotics (VGR) is rapidly becoming a facilitating technology for the automation of various processes within numerous different industries such as automotive, food & beverage, pharmaceuticals, chemicals, electronics, semiconductors, and plastics. A 3D VGR system processes parts randomly located across three dimensions and can accurately discover each part’s 3D orientation. For instance, in the automotive sector, the system is used for locating sheet metal body parts, power train components, complete car bodies, and other components used during assembly.
Following are the key trends in the market:
Automotive manufacturers are increasing their investments in vision systems. 3D machine vision is used by automakers and parts suppliers for various applications, including bin picking, error-proofing, material handling, robotic guidance, inline welding analysis, surface inspection, and traceability.
To achieve better image quality, component suppliers are opting for better optics. They are not just focusing on quality but also on processing speed. This has led to the introduction of a liquid lens. Liquid lenses can change focus almost instantly, utilizing a change in current or voltage without the mechanical changes of conventional lenses. By 2021, more component cameras are expected to be released with embedded processing, which will automatically control the liquid lens, making the technology more flexible than ever.
Embedded vision is another technology trending in the market. Embedded imaging enables the combining of image capture and processing capabilities into a single device. This machine vision technology is being deployed into several industrial applications, such as inspection and sorting systems. Manufacturers are integrating embedded vision into products like autonomous vehicles and drones.
3D Machine Vision systems form an integral part of the industrial IoT and enable transforming the operations. Imaging technologies such as 3D machine vision systems rely on low-latency, uncompressed data to make a real-time decision. Poor data quality or delivery can translate into costly production halts or secondary inspections, or a product recall that can harm the company's reputation. The application of machine vision in IIoT helps interpret abstract data used in decision-making or further automation. Along with this, as the sensors are becoming increasingly intelligent and supporting computer vision algorithms, the data produced offers valuable insights into the operation of industrial systems, which is helping manufacturers in decision-making for their business strategies.
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