Overcoming complexity
In truth, 3D machine vision is not a new idea. Manufacturers have looked at it as a possibility for at least two decades, largely so they could go beyond inspection of surface defects and see depths more effectively.
"3D machine vision has been around for a very long time," said David Michael, director of core vision and applications for Cognex, in a Design News interview. "The world is 3D, not 2D, so this is a direction that people want to move in."
KWD Automotive uses 3D machine vision on industrial robots that assemble car doors.
The path to 3D has been strewn with stumbling blocks, however. The mathematics behind it are complex, software libraries have been spare up to now, and robustness has been lacking. For many hopeful users, outside expertise was needed to get the job done.
"3D is hard," Petry said. "A lot of automotive companies have been working on this for 20 years. And a lot of vision companies have gone bankrupt trying to support the applications. It's an order of magnitude harder than 2D."
The good news, however, is that machine vision manufacturers believe they're crossing over into a new era, in which vast software application libraries are becoming more plentiful and 3D vision packages are growing more user-friendly.
"In the past, it had to be a very specialized person to make it go," Michael said. "But the trend now is moving from the specialized expert to a normal manufacturing engineer who can deploy this with enough training."
Cognex's VisionPro 3D, for example, incorporates a software library that is specifically designed for machine builders and plant automation engineers. It includes a software library with 3D shapes, mathematical tools, and calibration software for a variety of applications. Similarly, MVTec Software GmbH now offers Halcon 9.0 for 3D alignment and reconstruction, which helps to determine 3D shapes and orientation of objects.
Couture said that such systems enable manufacturers to address manufacturing problems that couldn't be touched with classical 2D solutions. He cites an example of a candy bar manufacturer who wanted to inspect chocolate bars to see if they contained almonds. By measuring the depth of the bar, he says, the manufacturer could tell if the almonds were present or missing.
"The whole bar is coated in chocolate, so you have very poor contrast," he said. "But with 3D, the almond pops out at you."
Check back next week for part 2 of this article, "Better Cameras Simplify Machine Vision."
I like your way of thinking, Naperlou. Combining all of those disparate, but related 3D technologies can certainly play a huge role in helping manufacturers address quality issues in an expedient, cost-effective, and continuous manner. Armed with that kind of real-time ability to respond and improve, perhaps American manufacturing can move forward in a positive way as Alex's comments suggest.
Thanks, Chuck. I'm sure the processing requirements are high but multi-core processors are perfect for this type of application. As Moore's Law marches on in automation controllers, vision is definitely one of the apps that should be able to expand by using the extra available processing horsepower.
I think wider adoption will still be slow, Al. The wild card, however, might be the economy. If auto sales jump, we could see wider adoption of 3D for a lot of different automotive applications, such as inspection of braking components, fuel lines and tire treads.
There's a convrgence of trends that are making 3D more viable, Rob. Availability of software libraries has been important, but equally important is the availability of multicore processors to run the applications. We're hearing of users who are employing 8, 10, and even 12 cores to do the processing. 3D is also getting a bit simpler. In-house engineer without 3D experience can do simple applications, such as de-palletizing. And now there are more system integrators who can handle robotics applications. It's still complex, but 3D no longer requires the expertise of Ph.D.-level specialists.
Nice article, Chuck. I look forward to the next one.
What was the tipping point on the adoption of 3D vision. Was it the cost of cameras coming down? Was it software developments that made it easier to deploy (i.e. users don't have to do complex original programming)? Or something else?
One question for 3D vision in automation applications is how much this technology expands the processing requirements required for these more sophisticated vision applications. With the move toward one controller, one network and PC-based systems where users are gaining confidence in the reliability of the real-time operating systems, a sharp increase in processing for 3D would potentially inhibit adoption at some level. Chuck, any ideas on the impact this might have from your research?
Chuck, thanks for a clear, comprehensive article on this subject. I think Alex's point is well taken: that the increase in the use of 3D techniques in MV are all about quality control, and good evidence for the revival of US manufacturing skills, ability and quality. Beth's and naperlou's comments are also intriguing: the implications of such convergence could be mind boggling.
Beth, I think you are right on with your comment. We are now moving into a design world where 3D design, printing and machine vision could streamline design and manufacturing and flow between them. Just imagine the possibilities for continuous improvement when you can combine the three.
This is the cover story in the February print issue of Design News. My comment would be, the increased quality control enabled by wider deployment of 3D vision plays into the whole meme of the resurgence of U.S. manufacturing. Not that this technology won't be used worldwide -- it will. But its deployment Stateside, particularly in the resurgent domestic automotive industry, will go a long way towards keeping U.S. manufacturing on par with its tough worldwide competition.
Chuck, Excellent article. This should be a very interesting area for automation and control over the next decade. As computing platforms and operating systems continue to move to the next level, vision should be an area ripe for innovation. Any ideas about how quickly we'll start to see wider adoption?
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