20.500.12556/RUL-146352 Training artificial neural networks using substructuring techniques application to joint identification The dynamic properties of assembled structures are governed by the substructure dynamics as well as the dynamics of the joints that are part of the assembly. It can be challenging to describe the physical interactions within the joints analytically, as slight modifications, such as static preload, temperature, etc. can lead to significant changes in the assembly’s dynamic properties. Therefore, characterizing the dynamic properties of joints typically involves experimental testing and subsequent model updating. In this paper, a machine-learning-based approach to joint identification is proposed that utilizes a physics-based computational model of the joint. The idea is to combine the computational model of the joint with dynamic substructuring techniques to train the machine-learning model. The flexibility of dynamic substructuring permits the enforcement of compatibility and equilibrium conditions between the component models from the experimental and numerical domains, facilitating the development of machine-learning models that can predict the dynamic properties of joints. The proposed approach provides an accurate data-driven method for joint identification in real structures, while reducing the number of measurements needed for the identification. The approach permits the identification of a full 12-DoF joint, enabling the coupling of 3D dynamic models of substructures. Compared to the standard decoupling approach, no spurious peaks are present in the reconstructed assembly response. The proposed approach is validated numerically and experimentally by reconstructing the assembly response and comparing the results with known assembly dynamics. joint identification dynamic substructuring frequency-based substructuring artificial neural networks physics-based computational model identifikacija spojev dinamsko podstrukturiranje frekvečno podstrukturiranje nevronske mreže true false true Angleški jezik Slovenski jezik Članek v reviji 2023-05-24 13:12:07 2023-05-24 13:12:13 2025-08-08 13:22:23 0000-00-00 00:00:00 2023 0 0 18 str. Vol. 198, art. 110426 Sep. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 681.5:538.913 1096-1216 10.1016/j.ymssp.2023.110426 153289219 Korbar...-Training_artificial_neural_networks_using_substructuring_techniques_bElektronski_vir_eapplication_to_joint_identification.pdf Korbar...-Training_artificial_neural_networks_using_substructuring_techniques_bElektronski_vir_eapplication_to_joint_identification.pdf 1 E374E4305B8AEB447E45B91A29EDE3D0 6d40f1051b4cf9f2521af75079ce01e4f1e5be8bb8e1864d4699d4fd17dbb98b fc5b2195-fa29-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=169522 https://www.sciencedirect.com/science/article/pii/S0888327023003345 1 8db51325-fa23-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=169518 Fakulteta za strojništvo 0 0 0