Biosensor and Chemical Sensor Technology:  Process Monitoring and Control Edited by Kim R. Rogers (US-EPA), Ashok Mulchandani (University of CaliforniaRiverside), and Weichang Zhou (Merck Research Laboratory). ACS:  Washington, DC. 1995. xii + 197 pp. $52.95. ISBN 0-8412-3330-6.

1996 ◽  
Vol 118 (33) ◽  
pp. 7872-7872
Keyword(s):  
Research Laboratory ◽  
Chemical Sensor ◽  
Sensor Technology ◽  
Washington Dc ◽  
Monitoring And Control ◽  
Merck Research Laboratory ◽  
Technology Process ◽  
Process Monitoring And Control ◽  
Us Epa ◽  
And Control

The Isolator of Industrial Automatic Control System Selection and Application

2011 ◽  
Vol 301-303 ◽  
pp. 1714-1718
Author(s):  
Ji Meng Zhang ◽  
Hong Shuo Wang ◽  
Ben De Gan
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Automatic Control System ◽  
Signal Transmission ◽  
Control Process ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Isolation Device ◽  
System Selection ◽  
And Control

In the automatic control system of industrial field, the production process monitoring and control process is dependent on Mutual coordination of various automation instrument, computer and corresponding actuators. The coordination is accurate or not, the key is signal transmission quality among those agencies. The application and selection of isolation device directly affect signal transmission. This paper discusses the application and choose of industrial site isolator from isolation principle, the principle and choose for isolator, commissioning and parameter selection based on practical application.

Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control

2018 ◽  
Author(s):  
Farhad Imani ◽  
Bing Yao ◽  
Ruimin Chen ◽  
Prahalada Rao ◽  
Hui Yang
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Experimental Studies ◽  
Precision Machining ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
Ultra Precision ◽  
And Control

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

Sign in / Sign up

Export Citation Format

Share Document