The Third Dimension

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In our cover story this issue, Contributing Editor Hank Hogan looks at various implementations of 3D vision for automation. Both the hardware and software for the technology have dropped in price and improved in capability, he writes, hastening the day when more companies will take advantage of 3D’s high-information yield. This is opening up new uses for 3D, such as bin picking, high-speed quality control, and managing robot-human interaction. “Improving Automation with 3D Vision” (read article).

The use of 3D optical vision systems also is the subject of an article by Howard Chung, Pia Böttcher, and Erik Klaas of 8tree. The article focuses on a single task: improving the efficiency and information yield of aircraft dent inspection. The way to achieve an easy-to-use and cost-effective machine vision tool for this task, the authors write, is to move away from traditional 3D optical metrology equipment. What works is equipment designed to solve only one clearly defined problem, instead of making a tool that addresses a vast array of surface measurement tasks. It also helps to pay careful attention to the user-friendliness of the computer-human interaction. “Application-Specific Machine Vision Simplifies Aircraft Maintenance” (read article).

Elsewhere in the magazine

• Nora Bereczki of Optoforce describes one accessory that can expand a robot’s sense of touch, making it capable of tasks that typically require the dexterity of the human hand. Robot-mounted force sensors can automate assembly tasks that require high precision, such as pin insertion in automotive assembly. This can allow companies to automate monotonous, repetitive work for which human labor is hard to find. “Force/Torque Sensors Expand Robotic Capabilities” (read article).

• Adam Stern of Resonon writes about the use of hyperspectral vision systems to sort items with differences in color or appearance that are not readily discernible by the human eye. A combination of spectroscopy and imaging, hyperspectral imaging provides both spatial and spectral information. The resulting data, he writes, yields faster and more accurate results than human sorting and can provide more reliable classifications than conventional vision systems. “Hyperspectral Machine Vision Enables Smart Automation” (read article).

• Matt Pinter of Smart Vision Lights offers a Picks column about choosing the best illumination source for a machine vision application. In this, the choice of wavelength and lighting configuration often goes hand in hand, he says. But understanding how the electromagnetic spectrum interfaces with a specific product also provides critical information. “Understanding machine vision illumination” (read article).

Enjoy the issue.

Published: April 2018
EditorialMarcia Stamell

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