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 10 2 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.@en

Composites are increasingly used in the aerospace industry due to their lightweight potential and flexible design options. The most widespread manufacturing process for large components made of fibre composites is still the open mould process. In this process, a composite component is placed on a mould and hermetically sealed with a vacuum bag consisting of vacuum film and other auxiliary materials. The function of this vacuum bag is to evacuate possible air inclusions and to transfer the pressure evenly to the component during curing in an autoclave. If the vacuum bag is not tight, the quality of the final product can be affected. Leakages in the vacuum bag lead to porosities and defects in the component and must therefore be avoided by all means. Even though conventional leakage detection techniques are generally able to detect leakages in vacuum bags, they are usually considered as inaccurate and very timeconsuming. Within a market study, performed in this work, it is found that the most promising method for efficient and automated leakage detection is a combination of volumetric flow rate measurement and infrared thermography. The tightness of the vacuum bag is checked with the help of the volumetric flow rate measurement and, in the event of a leak, the affected area can be narrowed down with the help of the flow data. Afterwards, the exact position of the leakage within this area is determined by means of infrared thermography and can thus be remedied. In order to be able to assess the effects of leakage on vacuum bags, theory, experiment and numeric are subsequently linked to be able to make a valid statement about the condition within the vacuum setup. It is shown that both the volume flow rates and the pressure gradient that occurs as well as the temperature and air velocity in the area of a leakage can be simulated. These effects in turn have an impact on the subsequent component quality. To investigate their influence, various autoclave tests with different types of leakage are carried out and evaluated. It is shown that leakages where there is a direct connection between the laminate and the environment are significantly more critical, regarding porosities, than those where the laminate is still protected from inflowing air by an intact release film. The use of machine learning in leak detection subsequently shows further potential for improving localisation, especially in the case of multiple leaks in the component. The costs of leakages in the composite component manufacturing process are typically hidden in the overall production costs. To address this, this research also examines the financial impact of leakages and the estimated financial benefits of implementing an advanced leakage detection process. Overall, this research shows that the improved leakage detection, analysis of the impact of leakage in the vacuum bag and on the component, and implementation of machine learning will increase the efficiency, effectiveness, and sustainability of the open mould process. @en