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Resin injection into reinforcement with uncertain heterogeneous properties: NDE and control

Host Institution: The University of Nottingham

Start Date: 1st October, 2019

Lead Investigator: Michael Tretyakov

Co-Investigators: Andreas Endruweit, Andrew Long, Marco Iglesias, Mikhail Matveev

Aims

The aim for this Core Project is to use in-process information from sensors during resin injection to develop an Active Control System (ACS) to counteract stochastic variations. Utilisation of in-process data will make it possible to move from a conventional process model to a “digital twin” for the Resin Transfer Moulding (RTM) process, to capture and estimate local deviations from the design for any manufactured part. This significant advancement will deliver a major step-change in composite manufacturing by reducing the cost and increasing the robustness of the manufacturing process, thus improving confidence in the parts quality.

The main objectives for this project are as follows:
• To develop, improve and test Bayesian Inverse Algorithms (BIAs) for online estimation of local permeability and porosity, using data from an in-process monitoring system during resin injection.

• To develop and test an ACS for the infusion of complex geometry preforms, based on information from sensors, physical models and the BIA to minimise defects and ensure robustness.

These objectives will deliver a deeper fundamental understanding of the manufacturing science, which is crucial for developing further fundamental step-changing technologies. Uncertainty Quantification (UQ) capabilities provided by BIA and ACS are of great importance for evidence-based decision making under uncertainty and risk management. This project will focus on RTM processing, but the methodology can be transferred to other areas of composite manufacturing, including prepreg consolidation and cure.
There is therefore an opportunity to share this expertise with the other Core Projects, such as the resin infusion process under investigation for “Automated Dry Fibre Placement” and the development of novel 3D textiles in “New manufacturing techniques for optimised fibre architectures”.

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