Today’s manufacturers are under pressure to be more flexible, reduce downtime and costs and increase efficiencies.

Artificial intelligence (AI) is one of the most hyped technologies of recent years, and while it promises new cost and process benefits for inspection applications, deployment remains a challenge.

Artificial intelligence is here, and it is can improve quality in a number of ways. It can prevent bad parts from being made, discover trends, and monitor machine performance.

Autonomous Mobile Robots (AMRs) are the latest innovation that have been transforming traditional robot tasks through increased flexibility and diversified applications, including their unique ability to navigate in an uncontrolled environment with a higher level of understanding. 

Machine vision is a key technology for highly automated and seamlessly networked processes in the context of Industry 4.0, a.k.a. the Industrial Internet of Things. The use of new artificial intelligence processes such as deep learning is gaining in importance. A great many benefits make the technology attractive, but it also has limitations.

Since the economy climbed out of the last recession, “Help Wanted” signs have become a common fixture near manufacturing facilities all over the United States. With 10,000 baby boomers reaching 65 each day, retirements are leaving a significant experience gap to be filled.

Picture a plant floor that is updating operators, managers and even other plants about potential machine problems. It would connect one machine to another and one system to the next. In order to maintain the highest quality, the systems would monitor any data that seems out of order or check on the line in process.

The explosive growth of robots shouldn’t come as a surprise to anyone, especially in manufacturing. We are not quite to the level of a ‘90s action movie, but robots are certainly popping up in a lot more places these days. What may come as a surprise is the many ways companies are now using these robots, especially when it comes to metrology.

Drishti’s human-centric AI drives digital transformation while making human operators more competitive against automation.

Manufacturers typically use two different techniques to assess part conformance and process control: either in-process gaging or final inspection. In-process gaging is measuring the part while it is still in the process of being manufactured and using that data to, sometimes, adjust the process, other times for part conformance.