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In modern structures, mechanical joints are ubiquitous, significantly influencing a structure’s dynamics. Frictional connections contained in a joint provide coupling of forces and moments between assembled components as well as localized nonlinear energy dissipation. Certain aspects of the mechanics of these friction connections are yet to be fully understood and characterized in a dynamical systems framework. This work applies a nonlinear system identification (NSI) technique to characterize the influence of frictional connections on the dynamics of a bolted beam assembly. The methodology utilized in this work combines experimental measurements with slow-flow dynamic analysis and empirical mode decomposition, and reconstructs the dynamics through reduced-order models. These are in the form of single-degree-of-freedom linear oscillators (termed intrinsic modal oscillators — IMOs) with forcing terms derived directly from the experimental measurements through slow-flow analysis. The derived reduced order models are capable of reproducing the measured dynamics, whereas the forcing terms provide important information about nonlinear damping effects. The NSI methodology is applied to model nonlinear friction effects in a bolted beam assembly. A ‘monolithic’ beam with identical geometric and material properties is also tested for comparison. Three different forcing (energy) levels are considered in the tests in order to study the energy-dependencies of the damping nonlinearities induced in the beam from the bolted joint. In all cases, the NSI technique employed is successful in identifying the damping nonlinearities, their spatial distributions and their effects on the vibration modes of the structural component.

To assist human users according to their individual preference in assembly tasks, robots typically require user demonstrations in the given task. However, providing demonstrations in actual assembly tasks can be tedious and timeconsuming. Our thesis is that we can learn user preferences in assembly tasks from demonstrations in a representative canonical task. Inspired by previous work in economy of human movement, we propose to represent user preferences as a linear function of abstract task-agnostic features, such as movement and physical and mental effort required by the user. For each user, we learn their preference from demonstrations in a canonical task and use the learned preference to anticipate their actions in the actual assembly task without any user demonstrations in the actual task. We evaluate our proposed method in a model-airplane assembly study and show that preferences can be effectively transferred from canonical to actual assembly tasks, enabling robots to anticipate user actions.