Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding

Featured Application Predict race-tracking strength in an RTM process using minimal pressure sensor measurements and position the sensors optimally throughout the preform in order to do so.Abstract A Bayesian inference formulation is applied to the Resin Transfer Moulding process to estimate bulk permeability and race-tracking effects using measured values of pressure at discrete sensor locations throughout a preform. The algorithm quantifies uncertainty in both the permeability and race-tracking effects, which decreases when more sensors are used or the preform geometry is less complex. We show that this approach becomes less reliable with a smaller resin exit vent. Numerical experiments show that the formulation can accurately predict race-tracking effects with few measurements. A Bayesian A-optimality formulation is used to develop a method for producing optimal sensor locations that reduce the uncertainty in the permeability and race-tracking estimates the most. This method is applied to two numerical examples which show that optimal designs reduce uncertainty by up to an order of magnitude compared to a random design.

Automated summary

This article introduces a method to predict race-tracking strength in an RTM process using minimal pressure sensor measurements and position the sensors optimally. The method is proven to accurately predict race-tracking effects with few measurements and reduces uncertainty by optimizing sensor locations.