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Sensors in Flexible Manufacturing Systems
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problem by hypothesizing massively parallel computers that can perform matching in linear time. Examples of these approaches include graph matching, relaxation, and histogram analysis. The advantage of these applications is that the decision is based on all the available information at hand. The basic principle of the local-feature-focus (LFF) method is to find one feature of an image, referred to as the focus feature, and use it to predict a few nearby features to look for. After finding some nearby features, the program uses a graph-matching technique to identify the largest cluster of image features matching a cluster of object features. Since the list of possible object features has been reduced to those near the focus feature, the graph is relatively small and can be analyzed efficiently. The key to the LFF method is an automatic feature-selection procedure that chooses the best focus features and the most useful sets of nearby features. This automatic-programming capability makes possible quick and inexpensive application of the LFF method to new objects. As illustrated in Fig. 8.15, the training process, which includes the selection of features, is performed once and the results are used repeatedly.
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FIGURE 8.15
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The runtime phase of the LFF acquires images of partially visible objects and determines their identities, positions, and orientations. This processing occurs in four steps: 1. Reading task information 2. Locating local features 3. Hypothesizing objects 4. Verifying hypotheses The procedure (Fig. 8.15) is to input the object model together with the list of focus features and their nearby cofeatures. Then, for each image, the system locates all potentially useful local features, forms clusters of them to hypothesize object occurrences, and finally performs template matching to verify these hypotheses.
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An end effector on a welding robot (Fig. 8.16) contains an ultrasonic sensor for inspection of the weld. An ultrasonic sensor detects such flaws as tungsten inclusions and lack of penetration of weld. The end effector determines the quality of a weld immediately after the weld contact has been made, while the workpiece is still mounted on the weld apparatus; a weld can be reworked in place, if necessary. The delay caused by the paperwork and setup involved in returning the workpiece for rework is thereby avoided. The ultrasonic end effector can be mounted on any standard gas tungsten arc welding torch. It may also be equipped with a throughthe-torch vision system. The size of the ultrasonic end effector is the same as that of a gas cup with a cathode. A set of extension springs stabilizes the sensor and ensures that its elastomeric dry-couplant pad fits squarely in the weldment surface. The sensor can be rotated 360 and locked into alignment with the weld lead. A small force-actuated switch halts downward travel of the robot arm toward the workpiece and sets the force of contact between the sensor and the workpiece.
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End Effector Sound-Vision Recognition Sensors
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The sound recognition sensor consists of a source that emits sound waves to an object and a sound receiver that receives the reflected sound waves from the same object (Fig. 8.17). The sound recognition sensor array consists of one sound source and one to as many as 16 receivers fitted intricately on an end effector of a robot. The sound-vision recognition sensor array measures reflections from some surface of interest on the object, called the measured surface,
Sensors in Flexible Manufacturing Systems
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FIGURE 8.16
End effector on a welding robot.
which is perpendicular to the sound waves emitted from the sound source (Fig. 8.18). Four conditions govern the performance of soundvision sensors: Standoff Large surfaces Small surfaces Positioning
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FIGURE 8.17
End effector sound-vision recognition system.
FIGURE 8.18 The measured surface should be perpendicular to the sound waves emitted from the sound source.
Standoff
Standoff is how far the array must be located from the measured surface. The standoff, like other measurements, is based on the wavelength of the sound used. Three different wavelengths are used in the sound-vision sensor recognition system. The array standoff d should be one or two wavelengths s from the measured surface for the highest accuracy. The standoff can be as great as 12 wavelengths, albeit with reduced accuracy (Fig. 8.19). 1 . 5 s d 12 s (8.1)
where s is the sound wavelength and d is the standoff distance. The typical standoff distance in terms of frequency and wavelength is described in Table 8.1.
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