deeply embedded in the integrated manufacturing movement. High-end vision systems have proliferated, as a result of ever-growing computing power and better digital cameras, lasers and lighting technology. One ramification of this power is that vision suppliers have been able to simplify programming by capturing a fair amount of their applications expertise in software.

“We have programmed 150 functional modules,” says Dwight Carlson, chairman of Coherix Corp., an Ann Arbor, Mich.-based vision supplier that specializes in three-dimensional (3D) technology. “Today, a trained technician can use a simple drag-and-drop technique to configure a sophisticated machine vision system in a matter of a few hours. So, the cost of developing applications has plummeted.”

Another advantage of greater computing power is that the processors can crunch much more data faster to do more advanced inspections, such as laser-based digital holography. “Classical machine vision needs some feature, whether it’s a lead, ball or bracket,” says Carlson. An example of a featureless job suited to holography is measuring the flatness of an engine head in-line. Holography can do it to a micron within 20 to 40 seconds.

Coherix and others also exploit today’s computing power to process data from multiple stereo cameras to perform 3D inspection. This technology is useful for looking for cracks and chips in semiconductor conductor packages and for checking whether their leads are coplanar and will touch contacts at the same time.

Another application is the pallet inspection system developed by Nagle Research, an engineering firm in Cedar Park, Texas. The firm was asked to automate the grading of wooden shipping pallets, giving those with cracks or broken slats a lower grade than those without. The firm’s engineers would have to encode the intelligence necessary to recognize protruding nails, loose boards, and cracked or broken boards. The point was to detect the many small defects that can go unnoticed by human inspectors.

A 3D camera was necessary to capture the geometry of the pallet. Simply looking for changes in color or contrast, as 2D vision would, would not work here because the material is wood. Not only is contrast low between the nails and boards, but the color and grain patterns also vary greatly. “Every pallet is like a fingerprint in that no two are alike, even new ones,” notes John Nagle, president. For these reasons, 2D machine vision tends to give too many false positives.

Another problem is that the color of wood has little to do with the condition of the material. “For example, grain patterns are very difficult for a 2D system to distinguish from actual cracks,” says Nagle. A 3D system avoids these problems by looking at the geometry of the wood, rather than its color or contrast.

For this job, the engineers selected the Ranger 3D camera from Sick Inc., in Minneapolis, because it takes more than one type of image. It provides range or height data, a 2D high-resolution line scan, and a scatter image based on laser light spread along the surface. “The scatter image enables defects and cracks on the pallet to be spotted before a major defect is visible to the human eye,” says Jim Anderson, Sick’s vision product manager.

The multi-camera system analyzes the data in about a half-second per pallet. Not only does it give consistent results, but it also records the important metrics as the pallets are inspected, making it an essential set of eyes for identifying systemic defects and estimating material consumption.

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