Image processing techniques are needed to extract critical information pertinent to nano material characterization but the current processing methods are slow, expensive and labor intensive. There is a strong need to develop fast and reliable methods, enabling process control compatible automated processing of nano images. The authors believe specialized techniques are needed to address the challenges, and will discuss the recent development of nano image processing methods as well as the near- and medium-terms needs in the area of nano metrology and imaging. The authors will share their broad perspectives on this research direction.
Recent innovations in sensor technology enable manufacturers to distribute redundant sensors in manufacturing processes for quality monitoring, defect detection, and fault diagnosis. Even if a single sensor is relatively reliable, the large number of sensors in a distributed sensor system confronts us the almost unavoidable possibility that some of the sensors may malfunction. Without isolating sensor anomalies from the underlying process changes, abnormal sensor readings can cause frequent false alarms and jeopardize productivity. Traditionally, sensor system reliability has been ensured by employing off-line gage Repeatability and Reproducibility (R&R) calibration. But this off-line approach can be time consuming and costly for in-process distributed sensor systems. This paper will present a robust estimation procedure that automatically identify the observations related to suspected sensor failures. We first identify sensor redundancy and introduce an existing algorithm to assess the redundant level. We further suggest a decomposition technique, which helps to substantially reduce the computation expense of the existing algorithms for a large sensor system. Finally, the concept and procedure is illustrated using a distributed coordinate sensor system in a multi-station manufacturing system.
Since damage usually reduces stiffness, frequency responses of a structure can be used to detect damage where the variation/decrease in natural frequencies is used as indicator. Although frequency responses are easy to measure with a small number of sensors, they are global reflections of the system dynamic property and one single FRF oftentimes lacks the sensitivity for local damage detection. In addition, locating damage is difficult as it normally requires a large number of measured natural frequency shifts. In this paper we propose a new frequency response based approach by integrating piezoelectric transducer with variable inductance to the host mechanical structure. A piezoelectric shunt circuit could significantly change the system dynamic responses. While frequency responses can be easily measured for such system (for example, by applying voltage to the piezoelectric actuators), varying the inductance can lead to a family of frequency responses. By doing so, one can get much more complete “scanned” reflection of the possible damage within the mechanical structure. The family of frequency responses provides a much larger dataset for more accurate and reliable damage detection. In this paper, an inverse sensitivity based detection algorithm is formulated and the performance improvement due to the integration of variable inductive piezoelectric shunt is demonstrated.