Analysis and Minimization of Race Tracking in the Resin-Transfer-Molding Process by Monte Carlo Simulation

Abstract

A numerical analysis of the influence of race tracking on dry spots formation and the accuracy of permeability measurement during the resin-transfer-molding process is presented. In the numerical simulation of the mold-filling process, defects are randomly generated, and their effect is assessed by a Monte Carlo simulation method. The effect of race tracking on the unsaturated permeability measurement and dry spots formation is investigated on flat plates. It is observed that the race-tracking defects located near the injection gate increase up to 40% of the value of the measured unsaturated permeability. The race-tracking defects located near the air vents are more likely to generate dry spots, whereas those near the injection gates have a less significant influence on dry spots generation. Depending on vent location, it has for instance been shown that the dry spot area can increase by a factor of 30. Dry spots may be mitigated by placing an air vent at a suitable location based on the numerical analysis results. Moreover, those results may be helpful to determine optimal sensor locations for the on-line control of mold-filling processes. Finally, the approach is successfully applied to a complex geometry.

Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding

“…Although Equation ( 26) is the posterior, we need some way of interpreting this distribution. The posterior could be analysed using Markov Chain Monte Carlo (MCMC) methods [8], but this would involve evaluating the posterior hundreds of thousands of times. Calculating the posterior is a computationally expensive operation because it requires calculating G(k) and running the RTM model.…”

“…The unpredictable nature of race tracks, in terms of their strength, can lead to significant variations in resin propagation, significantly changing the flow front movement pattern [6][7][8]. This can lead to dry spots in the composite parts, as the flow moves in an unpredictable way, trapping air.…”

“…Research around the effects of race tracking is not mainly focused on the prevention of race tracking, which is inevitable to some extent, but on understanding and predicting the race-tracking effect. Techniques from areas of statistics and machine learning have been incorporated into prediction, as well as methods that incorporate existing physical models of RTM [8][9][10][11][12].…”