机器视觉在制造空间 - 质量技术的影响manbetx官网手机登录

根据MarketWatch,machine vision systems market预计到2023年将达到1353亿美元的价值。大多数需求来自制造空间。制造公司在装配线,高速生产,危险环境和检查中使用机器视觉。

Different Types of Inspections




Automated Inspection


A machine vision system can examine microscopic details and complete the inspection process with greater reliability, quicker speeds, and lesser error. But manual inspection has the capacity to use judgment, learn on the job, and manage variations.

Deep learning and AI can be integrated into machine vision. These technologies use neural networks that mimic human intelligence to distinguish anomalies, parts, and characters while tolerating natural variations in complex patterns. At the same time, they offer the speed and robustness of a computerized system.

Also, Read7机器视觉的应用

Challenges in Traditional Inspection

The inspection process identifies defects if any, and if the features of the product or the manufacturing process are as per specified requirements. This is done to prevent rework, damage to brand reputation, injury, fatality, loss of equipment, excess cost, or a loss of customers. Traditional inspection depends on manual labor, which is prone to burnout, errors, and subjectivity. Moreover, it is not easy to scale up manual quality control operations.

传统的机器视觉方法diffic找到它ult to interpret the quality of complex scenarios. They do not possess the capability of humans to make adjustments and understand that variations do not necessarily mean defects. They cannot check for unanticipated defects. If they have to be customized for every scenario, there will be many parameters and thresholds. This can make the system unwieldy or prone to performance issues or even a breakdown.

Also, ReadAutomated Inspection with the help of Machine Vision and AI Technology





Also, Read人工智能(AI)与机器学习(ML)VS深度学习(DL)


An AI-based machine vision system can be trained with thousands of data points so that it recognizes the difference between defects and accepted deviations. It can capture new data points (statistical data, images, etc.) to achieve extremely high and reliable identification rates. This can be used in defect detection and parts positioning. It can self-learn to identify features and classify data appropriately to enhance the assembly process and inspection process.

Automates the Data Capture and Reporting


Also, Read3通过人工智能解决的机器视觉应用





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