Artificial intelligence and predictive modeling still require a human element: Especially staff to capture data, manage insights, deploy the software, guarantee production quality and more.

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.

How you can identify opportunities for AI in your machine vision applications.

In roundness measurement – measuring the roundness of a part to confirm that it does not deviate from the known specifications on the blueprint – complexity has increased as parts become more complicated with tighter tolerances.

Nano Dimension Ltd. announced that it has signed and closed a definitive agreement to acquire Formatec Holding B.V, which includes its two subsidiaries - Admatec Europe B.V. and Formatec Technical Ceramics B.V.

It is beneficial to look to the experience of industry experts who have already taken many steps to provide a combination of AI and computer vision solutions.

Unlike standard vision industrial cameras, AI or inference cameras belong to the device class of “embedded vision systems.” This presents manufacturers with completely new challenges.

Deep learning is now more user-friendly and practical than ever and together with other vision technologies opens up new application areas, making the inclusion of vision inspection as part of Industry 4.0 even more beneficial.  

In this article, deep learning refers to developments during the last few years that have enabled applying the technique to entire images in the industrial machine vision space.

In all types of industries, machine learning (ML) tools are finding the needle in the haystack of data, augmenting quality and safety professionals with a new kind of intelligence that can unlock hidden data patterns that are impossible for the human mind or eye to absorb.