How to Easily Deploy AI in Factory Automation

In-Sight 2800 vision system superimposed over lab environment

Front facing In-Sight 2800 vision systemOften times, line and automation engineers would like to use machine vision and AI to improve their inspection rate and accuracy, accelerate their lines, and eliminate the need for manual inspection. However, there is a common misconception that implementing this technology is complex and requires either specific technical knowledge or the assistance of machine vision experts. That’s no longer the case.

AI is now easier to use than ever with the introduction of new technologies like edge learning. Edge learning is a subset of AI in which processing takes place directly on-device, or "at the edge," using a set of pre-trained algorithms.

Optimized for the needs of factory automation, the In-Sight 2800 vision system uses edge learning technology to solve a range of applications, quickly and easily. The system can be deployed within minutes and requires no deep learning or machine vision experience.

Being a newer technology, you may have questions around edge learning and how it’s deployed using the In-Sight 2800. Below we compiled a list of the most frequently asked questions and answered them.

What level of expertise is required to use edge learning?

Anyone on the factory floor can set up edge learning – line engineer, engineering supervisor, process engineer, or quality inspection engineer. What’s needed is not vision knowledge, but line and product knowledge. If you understand what makes your part defective or acceptable, or what classes parts need to be sorted into, you know all that’s need to use edge learning.

How long does it take to train someone to use edge learning on In-Sight 2800?

Learning the full capabilities of the In-Sight 2800 might take a couple of hours. But learning how to use its basic capabilities takes very little time. An engineer can plug in the system, point it at the part they want to inspect, capture training images, and be producing useful output in less than ten minutes.

In-Sight 2800 is designed for factory floor use, and a significant element of that design is the point-and-click EasyBuilder development environment. The intuitive interface provides the feedback necessary for optimizing the process, while allowing the user to experiment with the effects of various choices, such as different lighting colors (red, green, blue, white) or focus points.

EasyBuilder software interface showing inspection of electrical connector

How hard is it to set up an image?

The In-Sight 2800 makes it easy to optimize the appearance of your part or region of interest. At the 00:30 mark, the video below demonstrates how this is done.

What does it mean to classify something?

“Classifying” is the process of assigning useful categories to parts for purposes of inspection. In inspection, those categories are often OK/NG, separating acceptable (passing) from unacceptable (failing) parts by detecting defects. Classification can also involve sorting products into multiple categories, such as different part variations, or different configurations of a kit containing multiple objects.

In-Sight 2800 vision system inspecting blister pill packs and classifying scents on soap boxes

What’s an example of how to use edge learning-based classification?

Say your product is a container that includes a scoop for measuring the contents. Each container must have one, and only one, scoop included. When the containers come down the line, the scoops can be at a variety of angles or partly concealed by the container contents.

Simply capture three or four images of each condition using the In-Sight 2800 (or upload existing images): no scoop, one scoop, more than one scoop, and label which conditions are acceptable and which are not. The In-Sight 2800 will make its own decisions on how to differentiate these and the EasyBuilder interface will show you how confident it is of its categorization.

In-Sight 2800 vision system mounted above conveyor belt inspecting protein powder containers

If you miscategorize one of the images, you will see the confidence level drop, and you can run further analysis to find out why. Perhaps you missed a scoop in one of the cans and labeled that image as “no-scoop.” Relabel that image as the single-scoop condition, and the confidence level will go back up. That’s it. 

After this brief training, the In-Sight 2800 will accurately categorize each container. It can issue alerts, store the data, or send the information down the line so that unacceptable containers to be diverted. It then provides you with a statistical breakdown useful for process improvement.

Can I implement this technology with my current staff?

Using the In-Sight 2800 requires no specialized knowledge. The only knowledge required is what your staff already has: what distinguishes an acceptable from an unacceptable part, what classes your products come in, and when a product change requires updating inspections and a brief retraining of the In-Sight 2800.

So anyone working on your line or factory floor will be able to use the In-Sight 2800 with only a few minutes required to familiarize themselves with the interface.

How do I integrate the In-Sight 2800 into my current line?

The In-Sight 2800 has everything you need for immediate operation. It includes a high-resolution sensor, fast processor, a multi-color lighting, the option for a high-speed liquid lens, and an intuitive, point-and-click interface. Beyond this, all the system needs is power and a data connection. It can be up and running in minutes, and it compact size is designed to fit in even the most space-constrained line.

Learn how to deploy the In-Sight 2800 in 6 steps:

Mary Romano

Manager, Global Content Marketing, Cognex

A self-proclaimed marketing maverick, Mary has experience in a variety of industries and marketing activities. Her career began in the world of defense and aerospace, then shifted to collaborative robotics and now machine vision. She drives brand awareness, nurtures customer relationships, and elevates the impact of sales through compelling content. Outside of the office, Mary enjoys baking (and eating) desserts, running outdoors, and exploring new places. She is also a self-published author of a children’s book about her dog, Doma.

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