The role of machine vision in the manufacturing industry

With the continuous advancement of technology, domestic manufacturing companies have widely adopted machine vision, which can greatly reduce product failures and improve the overall quality of the production line. Let’s take a look at the role of machine vision in the manufacturing industry:

Predictive maintenance

Manufacturing companies need to use various large-scale machinery to produce large quantities of goods. To avoid equipment downtime, certain equipment must be monitored regularly. Manually inspecting every piece of equipment in a manufacturing plant is not only time-consuming, but also costly and error-prone. The idea is to only repair the device if it malfunctions or something goes wrong. 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 steps to prevent failures? Let’s take a look at some production processes that take place in high temperatures and harsh environments, where material degradation and corrosion are prevalent . As a result, the device deforms. If not addressed promptly, it can result in significant losses and the shutdown 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 indicate corrosion/overheating, the vision system can notify the appropriate supervisor, who can then take preemptive maintenance measures.

Cargo inspection

Manufacturing companies can use machine vision systems to detect faults, cracks, and other flaws in physical products. Additionally, these systems make it easy to check accurate and reliable component or part dimensions while manufacturing a product. Images of the cargo will be captured by a machine vision system. Trained machine vision models compare these photos to acceptable data limits and either pass or filter out the shipment. Any errors or defects will be communicated via appropriate notifications/alerts. In this way, manufacturers can automatically improve product quality through machine vision.

Barcode scanning

Manufacturers can automate the entire scanning process by equipping their machine vision systems with enhanced features such as Optical Character Recognition (OCR), Optical Barcode Recognition (OBR), Intelligent Character Recognition (ICR), etc. With OCR text contained in photo labels Likewise, packages or documents can be retrieved and verified against a database. In this way, products with inaccurate information can be automatically identified before leaving the factory, thus limiting the margin of error. This process can be used for applications involving pharmaceutical packaging, beverage bottle labels and food packaging information such as allergies or validity period) information.

3D vision system

Machine vision inspection systems are used in production lines to perform tasks that humans find 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, vision systems capture images from all angles to generate 3D models. When these images are combined and fed into AI algorithms, they detect any faulty threads or tiny deviations from the design. The technology has high credibility in manufacturing industries such as automotive, oil and gas, and electronic circuits.

Vision-based die cutting

The most widely used die-cutting technologies in manufacturing are rotary and laser die-cutting. Rotary uses hard tools and steel blades, while laser uses a high-speed laser. While laser die cutting is more accurate, cutting tough materials is difficult, while rotary cutting can cut any material.

To cut any type of design, manufacturing can use machine vision systems to perform rotary die cutting that is as precise as laser cutting. After the design pattern is fed to the vision system, the system directs the die-cutting machine (whether laser or rotary) to perform precise cuts.

Machine vision, assisted by artificial intelligence and deep learning algorithms, can effectively improve the work efficiency and high-precision requirements of the manufacturing industry. After this model, controller and robotic technology are combined, the manufacturing production supply chain can be monitored All situations that occur within the machine, from assembly to logistics, require minimal human interaction during this period. This avoids errors caused by manual procedures and allows corporate employees to focus on higher-level cognitive activities. Therefore, machines The importance of vision to manufacturing is irreplaceable, and it will be a new revolution.

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