Embedding Intelligence for RTM Process Monitoring and Control

R. Caponetto; Gianluca Cicala; G. Dongola; F. Filippino; G. Recca

doi:10.4028/www.scientific.net/AST.56.530

Paper Titles

Multivariate Statistical Analysis for Detection and Identification of Faulty Sensors Using Latent Variable Methods
p.501

A Numerical Method for the Dynamic Analysis of Buildings Provided with Viscoelastic Devices
p.508

Optimization Algorithms for System Integration
p.514

Linking the Impact Force History with Residual Mechanical Characteristics and NDI - A Smart Way to Perform SHM in Composites
p.524

Embedding Intelligence for RTM Process Monitoring and Control
p.530

Biologically Inspired Shape Changing Aerodynamic Profiles and their Effect on Flight Performance of Future Aircraft
p.534

Innovations in Smart Materials and Integrated Structural Health Monitoring in the Australian Defence Force
p.545

Vibration Damage Reduction of On-Board Electronic Boards Using Modal Active Control
p.551

Monitoring Data for Retrofit Assessment
p.557

HomeAdvances in Science and TechnologyAdvances in Science and Technology Vol. 56Embedding Intelligence for RTM Process Monitoring...

Embedding Intelligence for RTM Process Monitoring and Control

Abstract:

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.