@article {Dare:2020:0736-2935:5682,
title = "Experimental force reconstruction using a neural network and simulated training data",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2020",
volume = "261",
number = "1",
publication date ="2020-10-12T00:00:00",
pages = "5682-5688",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2020/00000261/00000001/art00076",
author = "Dare, Tyler",
abstract = "Force reconstruction is the determination of unknown applied forces using operational vibration information. This inverse problem is made more difficult by limiting the number of operational sensors and increasing the number of modes excited in a given frequency range. Artificial neural
networks have been generated to do a wide variety of tasks in vibrations, including inverse problems. However, the amount of training data required is usually too much for a single experiment to generate. In this research, a neural network was generated to estimate the location of a hammer
impact on a plate using accelerometer data. Instead of experimental data, however, the network was trained on a finite element model of the structure. To account for variations between the model and reality, the training data was generated with a range of physical properties, such as material
stiffness and damping. The resulting model was able to identify the location of a hammer impact to within approximately 5% of the size of the plate.",
}