3d Machine Vision Systems
Machine vision refers to applications in which the PC automatically makes a decision based on visual input from a camera. Machine vision is a term typically used in industrial manufacturing, where applications range from culling blemished oranges from a conveyor belt to saving lives by inspecting to ensure that the correct drug capsule has been placed in the package before the product is shipped to the pharmacy. Three dimensional vision based measurement systems have made their presence into production metrology applications, notably in the electronics field. However, in the more traditional fields of durable goods now dominated by hard gauges and CMMs, 3D optical systems has been hindered by perceptions and real limitations. This paper will review where 3D vision is today, and what advances have been made to enable more quantitative, shop floor metrology applications. The field of 3D machine vision is a less established field, but one that is actively growing today. Three dimensional vision based measurements have come a long way in the past few years, moving from purely visualization tools that generate attractive color pictures, to serious measurement tools. These 3D systems include laser scanning, structured light, stereo viewing, and laser radar just to name a few.
As has already been stated, the key operational parameters needed for production machine vision include speed, resolution, and robustness especially to changing part surface conditions. Many systems that provide the best resolution are not the fastest, so a tradeoff must be made. Just as with touch probes, there are certain types of features or surfaces that optical 3D methods can be expected to work good on, and others where there may be problems. If has been pointed out that shiny, but not specular surfaces have offered one of the biggest challenges. In like manner, when a surface changes from a shiny area to a dull, many sensors may generate a bias error. In the simple case of triangulation, the measurement is based upon finding the centroid of a light spot of some finite size. If half that spot is on an area that reflects back to the sensor well, and the other half is not, the center of brightness of the spot will not be the geometric center, but rather weighted toward the brighter region. Testing the sensor on edge and surface transition features is a valuable first test to consider
The next area of concern is the surface texture itself. A surface with machining marks has a texture which may scatter light into long lines that may confuse the sensor. In a similar manner, if the surface is translucent, the spot of light may spread out in an unpredictable manner, again offsetting the spot center, and hence the measurement made. Therefore, testing the sensor on a surface texture that matches the one to be measured is important. A final important feature consideration for many optical gages is the slope of the surface. A standard way to test such effects is to scan a diffuse sphere and fit the data to the known diameter (see Figure 7).At some angle around the sphere, the surface can be expected to lift off the true surface, before the signal is lost entirely. As with any gage, comparison of the optical gage against other measurement tools provides valuable information regarding confidence of the measurements. This is not always an easy comparison, as the repeatability of many traditional gages may not be as good as the optical gage.
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