Computational imaging technology has found its way into industrial automated inspection, where the creative use of illumination components has emerged as an enabling technology providing valuable imaging capabilities.
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.
Deep learning software represents a powerful tool in the machine vision toolbox, but one must first understand how the technology works and where it adds value.
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.