Since the beginning of modern industrial robots in the early 1980s, robots have been guided by machine vision. Originally there were only a few robots with vision, but today it is over 5,000 robots annually in the North American market and significantly more globally.
By applying DL with a Data-Centric Approach, Users Can Streamline Even the Most Challenging Manufacturing Steps with Fast, Accurate Automated Inspection.
A sub-discipline of artificial intelligence (AI), deep learning (DL) has become a breakout technology in high-profile market sectors such as retail and high-tech.
My informal observations of published white papers and interviews with colleagues support that quality is moving in the direction of Quality 4.0, but very slowly.
Automated robotics are the best way to mimic a human in a factory environment, experts say. A robot's automated camera could replace a person's eyes, a PC would represent the brain and robotic arms are movement.
The manufacturing industry has seen major upheaval over the past few years. From supply chain disruption to worker shortages, keeping the pipeline filled with quality products – free of defects – has been no easy challenge. Because of these issues, the promise of Industry 4.0, or smart manufacturing, has never been more important.
Ongoing labor shortages, easier-to-use robotic solutions and new industries embracing robotics, such as restaurants, retail, construction and even agriculture, led to a record number of robots sold in North America in 2022, at least through Q3. We expect these trends—and others—to contribute to the growth of automation in 2023.
We sat down with Dr. Arun Dalmia, founder and president of Active Inspection, a systems integrator based in Grand Rapids, MI, to ask him some questions on how system integration has helped his clients build robust and sustainable manufacturing processes and how the role of systems integrators is evolving.
Beyond the growth in applications brought about by improvements in CMOS sensor technology, another significant trend is the increase in applications that extend beyond the visible spectrum.
A smart camera in the machine vision market is defined by its system architecture, experts say. Specifically, a smart camera packages an imaging sensor, sensor interface, computer, and I/O interface into a single package.