Joint identification through hybrid models improved by correlations

In mechanical systems coupled with joints, accurate prediction of the joint characteristics is extremely important. Despite years research, a lot yet to be learnt about joints' interface dynamics. The problem becomes even more difficult when Degrees-of-Freedom (DoF) are inaccessible for Frequency Response Function (FRF) measurements. This is, example, case bladed-disk dove-tail or fir-tree type joints. Therefore, an FRF based expansion method called System Equivalent Model Mixing (SEMM) used obtain expanded uses numerical and experimental sub-models each component their assembly produce respective hybrid sub-models. By applying substructure decoupling these sub-models, can identified. However, noisy due measurement errors which propagate this paper, correlation approach proposed in SEMM wherein quality improved. new approach, several models generated systematically using different combinations FRFs computing parameter, Assurance Criteria (FRAC), evaluate contribution lowest correlated channels filtered out on certain threshold value FRAC. Using improved identification also shows remarkable improvement. test object disk one blade joint.