Resumen
One of the ongoing tasks in space structure testing is the vibration test, in which a given structure is mounted onto a shaker and excited by a certain input load on a given frequency range, in order to reproduce the rigor of launch. These vibration tests need to be conducted in order to ensure that the devised structure meets the expected loads of its future application. However, the structure must not be overtested to avoid any risk of damage. For this, the system?s response to the testing loads, i.e., stresses and forces in the structure, must be monitored and predicted live during the test. In order to solve the issues associated with existing methods of live monitoring of the structure?s response, this paper investigated the use of artificial neural networks (ANNs) to predict the system?s responses during the test. Hence, a framework was developed with different use cases to compare various kinds of artificial neural networks and eventually identify the most promising one. Thus, the conducted research accounts for a novel method for live prediction of stresses, allowing failure to be evaluated for different types of material via yield criteria.