As the field of machine vision grows, it becomes increasingly important to understand what makes a machine vision system work well.

A look into some of the latest trends in machine vision, in terms of both technologies and applications.

The phenomenal rise of collaborative automation systems over the past decade or so has seen collaborative technologies deployed on a growing number of quality control applications. Collaborative automation enables companies of all sizes to improve throughput and reduce cycle times on inspection tasks.

Manufacturers are now embracing the move to the digitization of their production processes. Many initial project goals are very narrowly focused around removing manpower, manual errors, and running 24/7 to improve total output. These are all worthy goals for sure. Yet, this approach leaves many facets in their overall process often overlooked. There are critical questions that must be asked.

Teledyne DALSA announced the all-new Genie™ Nano-10GigE M/C8200, and M/C6200, based on Teledyne e2v’s 67M and 37M monochrome and color sensors.

Smart Vision Lights (SVL) announced the release of its JWL150, the first in the new Camera-to-Light (CTL) series.

JAI introduced the Go Series GO-8105M-5GE-UV, a new 8.1-megapixel, UV-sensitive camera with a high performance 5GBASE-T (5 GigE) interface.

Seasoned technology veteran brings over 30 years of experience to the position.

In the machine vision marketplace the term “AI” typically refers to deep learning platforms that enable industrial automation and inspection. To appreciate the value proposition of AI in this context, it’s helpful to understand how the technology has evolved over the past several decades.

It is crucial to hit the right color tone in the production process and to produce it homogeneously across numerous batches. Color not only leaves an impression of quality, but can also be used as an indirect variable to control the process.