The official definition of “machine vision” encompasses all industrial and nonindustrial applications in which a combination of hardware and software provide operational guidance to devices in the execution of their functions based on the capture and processing of images. In short, machine vision helps companies manufacture quality goods, repeatably.

Lens and camera sensor technology tends to co-evolve. As cameras drive to smaller and smaller pixel sizes with growing formats, lenses need to be designed to match those higher capabilities. 

In today’s manufacturing environment, automatic vision inspection has been widely applied in many different industries including semiconductor, electronics, food and beverage, pharmaceutical packaging, automotive, and many others.

Machine vision processes have become standard practice in quality assurance. Inspecting reflective surfaces, however, presents a challenge. A technology known as deflectometry can be used to reliably detect all types of defect even in these circumstances.

Lighting and lighting control is a critical component of any machine vision system since it has a massive influence on the signal to noise ratio and contrast in the images acquired.

Sensors that report a factory’s performance by measuring machine functioning, downtime, and production goals are in high demand.

Thermal imaging can be used for quality control in many industries. It is a nondestructive inspection method, which is especially used to detect flaws that are not visible on the surface. 

The demand for machine vision has grown exponentially as manufacturing facilities turn to automated quality control solutions to remain competitive in fast-paced markets with decreasing tolerance for error. In fact, the rise of machine vision is directly correlated with the increase in automation and robotic use in factories.

Machine vision is a key technology for highly automated and seamlessly networked processes in the context of Industry 4.0, a.k.a. the Industrial Internet of Things. The use of new artificial intelligence processes such as deep learning is gaining in importance. A great many benefits make the technology attractive, but it also has limitations.

Russ Hudyma, Chief Technology Officer with Navitar, discusses the benefits of precision lens-to-sensor active alignment within the field of machine vision for high-end inspection.