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Due to rising energy costs, high efficiency motors have been increasingly in demand in the last decade. Furthermore, for reasons of compactness and (thus) weight, high speed machines are preferred. Finally, it is important that the motors have a low cost level. Unfortunately, designing a (high speed) machine for high efficiency does not automatically result in a low cost solution. For example, choosing very thin laminations for the stator stack results on the one hand in lower electromagnetic losses and higher efficiency. On the other hand, thin laminations are more expensive. This project investigates the influence on iron loss using the electromagnetic and magnetomechanical models of different thin laminations and solid structures with due consideration of the detailed aspects of both stacking and construction. This will be possible since the developed parametrized (semi-)analytical model will allow the investigation of the influence of geometrical parameters on iron losses by a ?semi-analytic? path and an ?empirical? path. This includes the detailed aspects of stacking and construction, possibly leading to magnetic material degradation, e.g. cutting or punching of laminations, glueing and bending of laminations, radial and axial pressure on the stack, interlocking and stacking shape in the lamination plane and out of the plane. This project envisions challenging research towards better models to predict parasitic electromagnetic effects, i.e. force distributions on lamination deformations and 2D and 3D iron losses. Based on this knowledge, new loss models for magnetomechanical interaction, empirical factors and measurement methods for determining the dynamic characteristics will be developed. Measurements will be carried out on three pre-prototype setups, using two approaches, i.e. microscale and macroscale iron loss analysis. Following this project, it should be straightforward for the involved companies to implement this extended iron loss model in their software and to compute the losses for their own applications.