Machine vision fuses machine learning with commercial-grade hardware to provide consumers and businesses with an unprecedented ability to perceive the environment. In addition to these technologies, there are automation and high-speed networks, which together constitute a new industrial revolution-Industry 4.0. They will bring a new way of low waste and high efficiency industrial activities to industry.
Every industry is exploring the benefits of machine vision
Machine vision will affect vertical areas such as manufacturing, drilling and mining. There will be further development in freight and supply chain management, quality assurance, material handling, safety, and various other processes.
Soon, machine vision will be ubiquitous, providing key intelligent technologies for the Internet of Things built by the industrial world. Below, let's see how it is put into practice.
What is machine vision?
Machine vision is a technology that allows machines to better perceive their surroundings. It facilitates higher-order image recognition and higher-level decision-making. In order to use machine vision, high-fidelity cameras are needed to capture digital images of the environment or the workpiece. These images can be captured by an autonomous vehicle (AGV) or robot. Then machine vision uses very complex pattern recognition algorithms to judge its position, identity or conditions.
Proper lighting is a key factor in achieving machine vision
There are several common light sources in machine vision applications, including LEDs, quartz halogens, metal halides, xenon, and traditional fluorescent lamps. If the barcode or part of the workpiece is blocked, reading may cause errors. Machine vision combines advanced hardware and software to enable machines to observe and respond to external stimuli in new and effective ways.
How does machine vision support corporate and industrial IoT?
The popularity of Industrial Internet of Things (IIoT) devices marks technological progress. IIoT enables enterprise business to gain unprecedented visibility from top to bottom. Based on network sensors and cloud resources, it provides two-way movement of local and remote assets between enterprises and business partners.
IoT hardware and software can generate valuable operating data, not only for small components like mechanical pistons or bearings, but also for large systems like truck fleets. Enterprises should look to any place with potential, even when resources or labor are in short supply.
Internet of Things means ubiquitous computing
Where is machine vision in the Internet of Things? Machine vision makes existing IoT assets more powerful and can better deliver value and efficiency. We look forward to creating more brand-new opportunities together.
Enhance the role of sensors: Machine vision makes sensors throughout the Internet of Things more powerful and useful. Sensors not only provide raw data, but also provide a degree of interpretation and abstraction, which can be used for decision making or to achieve a higher level of automation.
Reduce bandwidth requirements: Machine vision helps reduce the bandwidth requirements of large-scale IoT construction. Compared with the original method-capturing images and data from a data source, and then sending it to the server for analysis, machine vision is usually studied from the data source. Modern industry has generated millions of huge data. With the help of machine vision and edge computing, a large number of data points can be directly converted into executable operations without transmission to intermediate nodes for secondary processing.
Support IoT automation solutions: Machine vision complements IoT automation technology very well. Compared with QA staff, robot inspection works faster and more accurately, and once defects and anomalies are discovered, they will immediately provide relevant data to decision makers.
Improve the safety and practicality of robots / collaborative robots: Navigation systems built with machine vision give robots / collaborative robots greater autonomy and pathfinding capabilities, helping them work faster and more safely with humans. In underground warehouses and other high-risk environments, machine vision helps robots improve response time and reduce unnecessary errors and losses.
Make assets more transparent: Compared with traditional models, companies and industries now operate much less in terms of wasting time, materials and labor. Machine vision will continue to serve drones, material handling equipment, driverless vehicles, production lines, and inspection stations to better exchange detailed and valuable data with other components of the Internet of Things.
In a factory environment, this means that machines and personnel can better coordinate their work and reduce bottlenecks, friction, and other interruptions.
How do companies apply machine vision?
When you consider every step in a typical industrial process, it is not difficult to see that machine vision can improve every aspect of operation.
For example, when manufacturing an automobile part, humans and cooperative robots jointly control the quality of raw materials, and then transport them to the factory for processing. Only after the products have successfully completed the QA process will they be shipped to retailers or end users.
Whether the product is in a warehouse, in transit, or not yet assembled online, machine vision provides a complete set of automated processing processes. It not only improves the operating efficiency of each department, but also maintains a higher and more consistent quality level.
In real-world applications, companies have integrated machine vision into their workflow.
Some applications are very simple, such as drawing a line on the warehouse floor to allow unmanned vehicles to follow safely. There are also some machine vision tools that are more complicated, whether they are simple or not, they will bring new changes.
Excitingly, tasks that were once considered difficult or impossible to outsource to robots in the industrial world can now also be accomplished with machine vision. As mentioned above, picking boxes for user orders in the warehouse is an inherently risky process, and any screening error will damage goodwill and the interests of customers.
Among them, product packaging damage, item location changes and SKU logo (Stock keeping Unit, Stock keeping Unit) subtle changes are the biggest source of error, using machine learning to pick boxes is a very good choice.
Autonomous ordering robots now in use are close to 100%. They can safely navigate, check the parts and products in the cargo box, use the robotic arm to make the right choice, and ship to the sorting area or packaging area.
This means that the company is much less at risk of shipping damaged goods or incorrect SKUs. However, these products still do not exactly match what the customer ordered.
Automated quality assurance and inspection is another important aspect of machine vision and Internet of Things applications, and has quickly gained popularity.
In some modern manufacturing environments, it not only helps employers automate the quality assurance process and improve quality, but also does not need to sacrifice manpower. At the same time, automated inspection stations handle these repetitive tasks, while employees learn more skills that require cognitive skills.
By 2025, collaborative robots may account for 34% of all robot markets. This is largely driven by the development of machine vision technology and the need to eliminate as much as possible inefficiencies, inaccuracies and waste in modern industry.
Machine Vision and the Fourth Industrial Revolution
Machine vision is expected to continue to develop in the next few years and further promote Industry 4.0, which many people call the fourth industrial revolution. It is reported that Eyes products already have embedded board-level image processing and machine vision functions.
The improvement of machine vision capabilities will bring the wider application of the Internet of Things and Industry 4.0 technology, as well as a new model of enterprise digital intelligent development.