Machine vision technology is widely used in various industries, which is used in product quality inspection, defect detection, part identification, assembly verification and other fields to improve production efficiency and product quality. It can be used in medical image analysis, disease diagnosis, surgical assistance, drug research and development to improve diagnostic accuracy and therapeutic effect.

The use of machine vision technology to achieve intelligent shelf management, product recognition, real-time inventory monitoring, face recognition payment, etc., to improve the shopping experience and management efficiency. Machine vision technology is applied to crop growth monitoring, pest detection, fruit and vegetable sorting, etc., to improve agricultural production efficiency and quality.

hardware

Machine vision technology has a wide range of applications in the field of hardware manufacturing and testing, which can be used for the surface quality detection of hardware, detecting surface defects, defects, scratches and other problems to ensure that product quality meets standards. Accurate measurement of hardware dimensions and geometric features, including length, width, height, aperture, etc.

Detect gaps, cracks, deformation and other defects of hardware, find and eliminate defective products in advance, and reduce the rate of unqualified products. The machine vision system is used to verify the assembly process of hardware parts to ensure the correct position, orientation and connection of parts, and improve the accuracy of product assembly.

Identification and tracking of logos, bar codes, serial numbers, etc. on hardware to help production process and logistics management. In hardware production, hardware of different types, sizes and shapes is classified and sorted. Combined with robots and automation equipment, it can realize the automatic detection, assembly and packaging of hardware parts, and improve the automation level of the production line.

Automobile parts

Machine vision technology has many application scenarios in the manufacturing and testing of auto parts, and machine vision systems can be used to detect the surface quality of auto parts, such as detecting surface defects, defects, scratches and other problems of parts to ensure that product quality meets standards.

It can be used to measure the dimensions and geometric characteristics of automotive parts, including length, width, height, aperture, etc., to ensure that the parts meet the design specifications. Can detect gaps, cracks, deformation and other defects in auto parts, timely find problems and eliminate unqualified products, reduce the rate of unqualified products.

Auto parts type, model, identification, etc., to ensure that the correct parts in the correct assembly location. Assisting workers in locating, aligning and installing parts enables sorting and sorting of different types, sizes and shapes of automotive parts, helping to improve the efficiency and management of production lines.

Realize automatic detection, assembly and packaging of auto parts, and improve the automation level of production lines. The machine vision system is used to track and manage auto parts to realize the traceability of parts and the management of products in process.

Chip semiconductor

Machine vision systems can be used to detect defects in chips and semiconductor devices, such as impurities, damage, etc., to accurately measure the size, shape and geometric features of chips and semiconductor devices to ensure that products meet design specifications. The use of machine vision technology to identify and classify chips, judge chip type, quality and other information, to help automate production and sorting.

The machine vision system can be used for positioning, alignment and connection during the chip assembly process to ensure the correct assembly of the chip and improve production efficiency and product quality.

Machine vision technology is combined with thermal imaging technology to detect the temperature distribution and heat loss of chips and semiconductor devices. Combined with artificial intelligence algorithms, intelligent management, optimization and prediction of chip production lines can be achieved to improve production efficiency and quality.