Study on Development and Application of Intelligent Structure with Smart Materials

本课题的目的是为智能结构系统开发一种实用的、定量的结构健康监测技术。由于压电驱动器可以在很高的频率下驱动，而压电传感器对应变变化率非常敏感，因此引入了压电陶瓷贴片结构作为驱动器和传感器，以产生和接收在结构中传播的高频波。由于压电材料与结构之间的机电耦合，通过监测PZT的阻抗可以有效地捕捉结构中的物理变化，这项技术对于检测微小裂纹或连接松动等早期损伤的存在非常敏感。此外，还开发了一种通过测量纵波传播来评估损害位置和表征的先进技术。为了减少结构中PZT贴片的数量以降低布线成本，还研究了如何减少结构中压电贴片的技术。研究结果已在SPIE第六届智能结构与材料国际研讨会上公布，并开发了一套切割300 mm硅锭的实时切割状态监测系统，这是因为硅锭应该被切割成尽可能平坦和光滑的硅片表面。利用固定在硅锭支架上的压电式三轴载荷传感器测量周向、径向和法向切削力。结果表明，通过对高频信号进行快速傅立叶变换和小波变换，可以很好地预测锯片的切割状态，实验验证了该在线监测系统对300 mm硅锭切割机的评估是有效的。

The purpose of this project is to development a practical and quantitative structural health monitoring technique for intelligent structure system. Since the piezoelectric actuator can be driven at very high frequency and the piezoelectric sensor is very sensitive to the rate of strain changes, the piezoceramic patches structures were introduced as actuators and sensors to generate and receive a high frequency wave propagating in the structure. Since the electromechanical coupling between the piezoelectric material and the structure the physical changes in the structure can be captured efficiently by monitoring the PZT electric impedance this technique is very sensitive to detect the presence of incipient-type damage such as small cracks or loose connections. Furthermore an advanced technique for assessment of the location and characterization of damages by measuring longitudinal wave propagation was developed. In order to reduce the numbers of the PZT patches in the structure for cuffing down the cost of wiring, the technique how to reducing piezoelectric patches in the structure was also investigated. The obtained results have been presented at the SPIE's 6th Annual International Symposium on Smart Structures and Materials.Further, a real time cutting condition monitoring system on slicing 300mm silicon ingot was also developed because the silicon ingot should be cut to yield wafers surface as flat and smooth as possible. The circumferential, radial and normal cutting forces were measured by using the piezoelectric 3-axis load sensor fixed onto the mount of the silicon ingot. The results show that the cuffing condition of the saw blade is clearly predicted by analyzing the high-frequency signal with FFT and wavelet transforms, and the on-line monitoring system is validated experimentally useful for evaluation of 300mm silicon ingot slicing machine.