On-Line Mass Spectrometry:  A Faster Route to Process Monitoring and Control

1999 ◽  
Vol 38 (4) ◽  
pp. 1192-1204 ◽  
Author(s):  
Kelsey D. Cook ◽  
Kevin H. Bennett ◽  
Martin L. Haddix
Keyword(s):  
Mass Spectrometry ◽  
Process Monitoring ◽  
Monitoring And Control ◽  
Line Mass ◽  
Process Monitoring And Control ◽  
On Line ◽  
And Control

On-line calorimetry as a technique for process monitoring and control in biotechnology

1997 ◽  
Vol 300 (1-2) ◽  
pp. 225-236 ◽  
Author(s):  
Urs von Stockar ◽  
Philippe Duboc ◽  
Laurent Menoud ◽  
I.W. Marison
Keyword(s):  
Process Monitoring ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
On Line ◽  
And Control

scholarly journals Calibration in process monitoring by using unsegmented continuous-flow systems

1989 ◽  
Vol 11 (6) ◽  
pp. 260-265 ◽  
Author(s):  
M. D. Luque de Castro ◽  
M. Valcárcel
Keyword(s):  
Analytical Chemistry ◽  
Process Monitoring ◽  
Continuous Flow ◽  
Monitoring And Control ◽  
Attractive Option ◽  
Process Monitoring And Control ◽  
On Line ◽  
And Control ◽  
Flow Techniques

An overview is presented of the different aspects of the role of analytical chemistry in process monitoring and control. On-line monitoring is currently the most attractive option in this area, especially with unsegmented-flow techniques. In addition to allowing automation of these systems, the variety of ways in which calibration and recalibration can be performed allows their adaptation to any situation by using extremely simple, home-made manifolds. The most relevant designs are presented and critically discussed in this paper.

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.

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