Study on Development and Application of Intelligent Structure with Smart Materials

本课题的研究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。由于压电致动器可以被驱动在非常高的频率和压电传感器是非常敏感的应变变化率，压电陶瓷贴片结构被引入作为致动器和传感器，以产生和接收在结构中传播的高频波。由于压电材料和结构之间的机电耦合，通过监测PZT电阻抗可以有效地捕获结构中的物理变化，该技术对于检测诸如小裂纹或松动连接的初始型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伤位置和特征的先进技术。为了减少结构中压电片的数量以降低布线成本，本文还研究了减少结构中压电片的技术。研究结果已在SPIE第六届智能结构与材料国际研讨会上发表，并在此基础上，研制了一套真实的实时切割状态监测系统，用于对300 mm硅锭进行切割，以保证硅片表面尽可能平整光滑。利用安装在硅锭底座上的压电式三轴载荷传感器测量了硅锭的周向、径向和法向切削力。结果表明，通过对高频信号进行FFT和小波分析，可以清晰地预测锯片的切边状态，并通过实验验证了该在线监测系统对300 mm硅锭切片机的评估是有效的。

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.