In recent years, the adoption of machine vision in China has experienced explosive growth, especially in the manufacturing industry. Enterprises using machine vision can reduce product defects and improve the overall quality of production lines – both indispensable factors. Machine vision is the ability of machines to acquire images, evaluate those images, interpret them, and then make appropriate responses.
Intelligent cameras, image processing, and software are all part of a vision system. Thanks to continuous advancements in imaging technology, intelligent sensors, embedded vision, machine and supervised learning, robot interfaces, information transmission protocols, and image processing capabilities, machine vision technology can upgrade and transform the manufacturing industry in many ways. Machine vision improves product quality by reducing human error and ensuring accurate quality inspection of all goods on the production line.
Analysis suggests that by the end of 2028, the domestic machine vision market will reach a value of US$53.3 billion, and is expected to maintain a growth rate of 9.9%. In addition, high-end manufacturing enterprises have a more obvious demand for inspection, which will drive the demand for industrial machine vision under artificial intelligence technology and promote the market's development.
The role of machine vision in the manufacturing industry
Predictive maintenance
Manufacturing companies rely on various large machines to produce large quantities of goods. To avoid equipment downtime, certain equipment must be monitored regularly. Manually inspecting each piece of equipment in a manufacturing plant is not only time-consuming but also costly and prone to errors. The idea is to only repair equipment when it malfunctions or malfunctions. However, utilizing this technology to restore equipment can have a significant impact on worker productivity, manufacturing quality, and costs.
On the other hand, what if manufacturing organizations could predict the operating status of their machines and take proactive measures to prevent failures? Let's look at some production processes that take place in high-temperature and harsh environments, where material degradation and corrosion are prevalent. As a result, equipment deforms. If not addressed promptly, this can lead to significant losses and the halting of the manufacturing process. Machine vision systems can monitor equipment in real time and predict maintenance based on multiple wireless sensors that provide data on various parameters. If any changes in indicators suggest corrosion/overheating, the vision system can notify the relevant supervisors, who can then take proactive maintenance measures.
Goods inspection
Manufacturing companies can use machine vision systems to detect defects, cracks, and other flaws in physical products. Furthermore, these systems can easily and reliably inspect the dimensions of components or parts during product manufacturing. Images of the goods are captured by the machine vision system. A trained machine vision model compares these images to acceptable data constraints, then passes or rejects the goods. Any errors or defects are communicated through appropriate notifications/alerts. In this way, manufacturers can automate and improve product quality through machine vision.
Barcode scanning
Manufacturers can automate the entire scanning process by equipping machine vision systems with enhancements such as Optical Character Recognition (OCR), Optical Barcode Recognition (OBR), and Intelligent Character Recognition (ICR). Similar to the OCR text contained in photo labels, packaging or documents can be retrieved and verified against a database. This allows for the automatic identification of products with inaccurate information before they leave the factory, thus limiting the margin of error. This process can be used to apply information about pharmaceutical packaging, beverage bottle labels, and food packaging (such as allergy information or expiration dates).
3D vision system
Machine vision inspection systems are used on production lines to perform tasks that humans consider difficult. Here, the system uses high-resolution images to create complete 3D models of components and connector pins. As components pass through the manufacturing plant, the vision system captures images from various angles to generate 3D models. When these images are combined and fed into AI algorithms, they detect any faulty threads or minute deviations from the design. This technology has high reliability in manufacturing industries such as automotive, oil and gas, and electronic circuits.
Vision-based die-cutting
The most widely used die-cutting technologies in the manufacturing process are rotary and laser die-cutting. Rotary die-cutting uses hard tools and steel blades, while laser die-cutting uses high-speed lasers. Although laser die-cutting is more accurate, it is difficult to cut tough materials, while rotary cutting can cut any material.
To cut any type of design, the manufacturing industry can use machine vision systems for rotary die-cutting with the same precision as laser cutting. After the design pattern is fed into the vision system, the system will guide the die-cutting machine (whether laser or rotary) to perform a precise cut.
With the assistance of artificial intelligence and deep learning algorithms, machine vision can effectively improve the efficiency and high precision requirements of the manufacturing industry. When models, controllers and robotics are combined, all situations that occur in the manufacturing production supply chain can be monitored, from assembly to logistics. The need for human interaction during this process is minimal, which avoids errors caused by manual processes and allows employees to focus on higher-level cognitive activities. Therefore, the importance of machine vision to the manufacturing industry is irreplaceable, and it will be a new revolution.
