Embedding Intelligence for RTM Process Monitoring and Control

2008 ◽  
Vol 56 ◽  
pp. 530-533
Author(s):  
R. Caponetto ◽  
Gianluca Cicala ◽  
G. Dongola ◽  
F. Filippino ◽  
G. Recca
Keyword(s):  
Linear Trend ◽  
Characteristic Curve ◽  
Resin Flow ◽  
Monitoring And Control ◽  
Flow Front ◽  
Rtm Process ◽  
Capacitance Variation ◽  
Embedded Capacitor ◽  
Process Monitoring And Control ◽  
And Control

A technique to control the resin flow front in closed and open mould is described. The technique consists in embedding linear capacitive sensors to provide the position of the moving flow front. The embedded capacitor is made with two parallel wires while the resin, between the wires, acts as a moving dielectric which changes the capacitance values. Therefore the position of the resin is obtained from the measured capacitance variation. In order to validate the proposed methodology the sensor has been characterized and the obtained characteristic curve showed a quasi-linear trend. The shape of the resin front could be modified, if necessary, acting locally with controlled injectors.

Online Process Monitoring and Control by Dielectric Sensors for a Composite Main Spar

2015 ◽  
Vol 825-826 ◽  
pp. 936-943 ◽  
Author(s):  
Arne Breede ◽  
Maryam Kahali Moghaddam ◽  
Christian Brauner ◽  
Walter Lang ◽  
Axel S. Herrmann
Keyword(s):  
Online Monitoring ◽  
Resin Flow ◽  
Monitoring And Control ◽  
Part Quality ◽  
Process Monitoring And Control ◽  
Online Process Monitoring ◽  
Key Aspects ◽  
And Control ◽  
Online Process ◽  
Dielectric Sensors

An online monitoring scheme to determine viscosity and permeability for a composite main spar infusion is presented. During the manufacturing process, defects such as dry spots and pores can occur and lead to poor part quality. In this study, dielectric sensors are used to create the information needed to model the process. Several experiments have been carried out to investigate the correlation between the sensors signal and the viscosity and to derivate constitutive equations. The provided data and the derived permeabilities are integrated into a simulation model of a cross section of the composite main spar. As a result, the real permeability can be identified. The knowledge of these parameters is one of the key aspects for a prediction of the resin flow and therefore for an improved process 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