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Ultrasonic Stress Sensor Measuring Dynamic Changes in Materials
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An ultrasonic dynamic vector stress sensor (UDVSS) has recently been developed to measure the changes in dynamic directional stress that occur in materials or structures at the location touched by the device when the material or structure is subjected to cyclic load. A strain gauge device previously used for the measurement of such a stress measured strain in itself, not in the part being stressed, and thus provided a secondary measurement. Other techniques, such as those that
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Advanced Sensors in Precision Manufacturing
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involve thermoelasticity and shearography, have been expensive and placed demands on the measured material. The optical measurement of stress required the application of a phase coat to the object under test. The laser diffraction method required notching or sharp marking of the specimen. A UDVSS is the first simple portable device able to determine stress directly in the specimen itself rather than in a bonded gauge attached to the specimen. As illustrated in Fig. 6.27, a typical material testing machine applies cyclic stress to a specimen. The UDVSS includes a probe, which is placed in contact with the specimen; an electronic system connected to the probe; and a source of a reference signal. The probe assembly includes a probe handle that holds the probe, a transducer mount that contains the active ultrasonic driver and receiver, an ultrasonic waveguide transmitter and ultrasonic waveguide receiver that convert the electrical signals to mechanical motion and the inverse, and a cable that connects the probe of the electronics. When in contact with the specimen, the ultrasonic waveguide transmitter causes acoustic waves to travel across the specimen to the ultrasonic waveguide receiver, wherein the wave is converted to an electrical signal. The operation of the UDVSS is based on the physical phenomenon that the propagation of sound in the specimen changes when the stress in the specimen changes. A pulse phase-locked loop reacts to a change in propagation of sound and therefore in stress by changing
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FIGURE 6.27
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Ultrasonic dynamic stress sensor.
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its operational frequency. The component of that signal represents that change in voltage needed to keep the system at quadrature to follow the system change in stress. That signal provides the information on changing stress. The UDVSS can be moved around on the specimen to map out the stress field, and by rotating the probe, one can determine the direction of a stress. In addition, the probe is easily calibrated. The UDVSS should find wide acceptance among manufacturers of aerospace and automotive structures for stress testing and evaluation of designs.
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Predictive Monitoring Sensors Serving the CIM Strategy
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Computer-integrated manufacturing technology can be well-served by a predictive monitoring system that would prevent a large number of sensors from overwhelming the electronic data monitoring system or a human operator. The essence of the method is to select only a few of the many sensors in the system for monitoring at a given time and to set alarm levels of the selected sensor outputs to reflect the limit of expected normal operation at the given time. The method is intended for use in a highly instrumented system that includes many interfacing components and subsystems for example, an advanced aircraft, an environmental chamber, a chemical processing plant, or a machining work cell. Several considerations motivate the expanding effort in implementing the concept of predictive monitoring. Typically, the timely detection of anomalous behavior of a system and the ability of the operator or electronic monitor to react quickly are necessary for the continuous safe operation of the system. In the absence of a sensor-planning method, an operator may be overwhelmed with alarm data resulting from interactions among sensors rather than data directly resulting from anomalous behavior of the system. In addition, much raw sensor data presented to the operator may by irrelevant to an anomalous condition. The operator is thus presented with a great deal of unfocused sensor information, from which it may be impossible to form a global picture of events and conditions in the system. The predictive monitoring method would be implemented in a computer system running artificial intelligence software, tentatively named PREMON. The predictive monitoring system would include three modules: (1) a causal simulator, (2) a sensor planner, and (3) a sensor interpreter (Fig. 6.28). The word event in Fig. 6.28 denotes a discontinuous change in the value of a given quantity (sensor output) at a given time. The inputs to the causal simulator would include a causal mathematical model of the system to be monitored, a set of events that describe the initial
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