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

制造业通常遵循两种检查:

手动检查

它是人类运营商在大会期间视觉检查单位的过程。它们通常依赖于肉眼或显微镜。

Automated Inspection

这包括计算机视觉和机器视觉检查。电脑愿景传统上用于捕获图像并自动化图像处理以获得质量控制和保证。机器视觉检查具有某种形式的图像捕获硬件,可以包括馈送系统,光学系统或分离系统以及配备有专门软件的集成计算机来处理图像并生成输出。

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

基于AI的检查有助于克服这些挑战

人工智能包括神经网络和算法,使得机器视觉系统从捕获的数据学习并调整他们的检查能力而不明确地编程以执行某些任务。基于AI的检查在测量和对准中更准确。它可以在不同类型的数据上工作,如图像,音频文件,模型或文档。它可以执行复杂的操作,如阅读具有挑战性的条形码或比较不同的纹理。

AI如何优化检查过程

传统的机器视觉系统通过遵循内部嵌入的算法可靠且一致地执行。但随着缺陷的例外或类型的数量增加,它们的有效性下降。人工智能可以在这里有所作为。自学习算法可以区分异常从缺陷。他们可以适应耐受自然变化。它们是多功能的,足以继续进行检查过程,并在船舶地板进程中增加复杂性或变化。通过将人体检查的灵活性与自动检测系统的速度和可靠性相结合,AI可以通过组合人体检查的灵活性来优化检查过程。

Also, Read人工智能(AI)与机器学习(ML)VS深度学习(DL)

来自多个来源的数据分析

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

基于AI的机器视觉系统捕获大型相关信息和最小初始参数的大型数据集以处理数据。他们可以搅拌这些数据和培训并改善维护和检查算法。它们可以捕获巨大的结构化和非结构化数据,分类数据和标记数据。该数据和元数据用于有意义的了解制造设备的状态,使公司能够在维护,修理或更换方面做出决定。可以收集适当的数据并用于检查流程并检测缺陷。它们可以在具有像素级细微差别或具有挑战性的光学字符识别的细节上产生综合缺陷报告和图表,从而最大限度地减少质量控制系统中的误差。

Also, Read3通过人工智能解决的机器视觉应用

Conclusion

机器视觉系统将继续大幅发展。AI将继续纳入机器视觉系统中,以进行视觉检查,这些目的是在性质上更复杂,如化妆品检查,纹理的分类和可变特征位置和特征。基于AI的机器视觉系统也使用公司提供的大量数据而超高效。机器视觉系统越来越多地在制造空间中发挥重要作用,因为它们支持最佳的生产率,提高灵活性和更好的质量。

注册我们即将到来的免费网络研讨会

REGISTER NOW

Leave a Reply

安排呼叫