Deep Learning Quality in Manufacturing?

 Interesting thoughts here.    Much more at the link.  

 Deep Learning Has Reinvented Quality Control in Manufacturing—but It Hasn’t Gone Far Enough

AI systems that make use of “lifelong learning” techniques are more flexible and faster to train  By Anatoli Gorchet

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.

In 2020, we’ve seen the accelerated adoption of deep learning as a part of the so-called Industry 4.0 revolution, in which digitization is remaking the manufacturing industry. This latest wave of initiatives is marked by the introduction of smart and autonomous systems, fueled by data and deep learning—a powerful breed of artificial intelligence (AI) that can improve quality inspection on the factory floor.

The benefit? By adding smart cameras to software on the production line, manufacturers are seeing improved quality inspection at high speeds and low costs that human inspectors can’t match. And given the mandated restrictions on human labor as a result of COVID-19, such as social distancing on the factory floor, these benefits are even more critical to keeping production lines running.

While manufacturers have used machine vision for decades, deep learning-enabled quality control software represents a new frontier. So, how do these approaches differ from traditional machine vision systems? And what happens when you press the “RUN” button for one of these AI-powered quality control systems?

Before and After the Introduction of Deep Learning in Manufacturing

To understand what happens in a deep learning software package that’s running quality control, let’s take a look at the previous standard. The traditional machine vision approach to quality control relies on a simple but powerful two-step process:  ... "