29-11-2012, 06:00 PM
3D MACHINE VISION SYSTEMS AS SHOP FLOOR METROLOGY TOOL
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ABSTRACT
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.
INTRODUCTION
Modern day durable goods manufacturing have begun to embrace the concepts of digitization as a means to improve productivity and quality. Moving away from expensive hard gages made for specific parts; manufacturers are seeking the means to measure parts in a flexible manner, and capture the resulting measurements by digital means. For higher volume parts, such as from forging or fast machining operations, speed of gauging is still an issue.
This is the area where machine vision based tools start to excel. Machine vision in general has been used for everything from guiding the insertion of electronic chips on circuit boards to inspecting bottles at several per second in bottling lines. A natural extension of machine vision inspection is to provide programmable measurements for machined parts. In many applications, these measurements can be made in two dimensions for which there is an established based of machine vision tools working in the sub-thousandth of an inch range at multiple measurements per second. Each of these methods has their strong points and weak points for a given application.
The key performance parameters needed for durable good manufacturing include:
• Resolution in the mil, and sub-mil range
• Speeds sufficient to complete all measurements in a few seconds
• Ability to look at a wide range of surface types and finishes
This last point, the ability to look at a wide range of surface finishes has perhaps been the biggest limitation of 3D machine vision technology. In many cases, the surface needs to be diffuse to permit reflected light to be easily seen to achieve a good signal to noise ratio.
DISCUSSION OF TECHNOLOGIES
There are currently three basic approaches to three-dimensional machine vision:
• range finding including structured lighting
• stereo or binocular vision,
• gray scale or range finding methods.
TRIANGULATION
The most popular commercial versions of range finding use the triangulation method where a beam of light is projected onto the object's surface at some angle and the image of this spot or line of light is viewed at some other angle (see Figure 1). As the object distance changes a spot of light on the surface will move along the surface by:
APPLICATION TESTING
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.