Computational methods for leakage localisation in a vacuum bag using volumetric flow rate measurements

Abstract

The localisation of leakages during the vacuum bagging process of a composite is currently time intensive and reliant on human labour. The purpose of this article is to explore four computational methods for leakage localisation using volumetric flow rate (VFR) data to increase the automation of the process. The data used in this article is based on experiments on a square vacuum bag with four vacuum ports. The first method is based on potential flow theory to simulate the flow. In the second method, numerical regression was applied to find a relation between the VFRs and port-leakage distances. The third method consisted of calculating the fractional VFRs for every point on a grid and finding the grid point whose values most closely correspond to the observed fractional VFRs. The last method involved training a machine learning algorithm with experimental data. After the development of the methods, their performance was tested over the entire single-leakage dataset and compared. The results were in the same order of magnitude for all methods, with an area of 10^5 mm² for the 95% confidence interval and a distance of 10² mm for the average error. Further research is required for application to different vacuum bag shapes and multiple leakages cases. The method with the highest adaptability and performance is the method based on potential-flow theory. Machine learning and the potential-flow method is further applicable to multiple leakage localisation.