The elasto-mechanical characteristic of forming machines is crucial for a variety of forming processes. Numerical simulations are to be performed to understand the complex interaction between process, forming tool, and machine. The machine's elastic fingerprint depends on its design, accuracy of the components, and mounting conditions. Models only based on data of the machine design are limited. Therefore, effective FE models are to be developed to transfer the physical structure into a digital representation. The paper presents different methods of creating effective models for the characteristics of forming machines determined by measurements. Examples and potential of using these models are presented.

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The replacement of damaged journal bearings in forming machines eventuates in very high personal and material costs as well as long-term downtime. Moreover, new discontinuous forming processes (e.g. press hardening, pendulum operations) have a negative effect on the hydrodynamic pressure built up in the bearing and can conduct to failure. Therefore, users and manufacturers of forming presses are looking for ways to improve the safety of this key component. In this paper, the concept of an intelligent hybrid plain bearing (IHPB) that combines hydrodynamic and hydrostatic characteristics, presented in [1], is detailed and validated on a test rig. The clearance state of the journal bearing is monitored through eddy current sensors and a controlled hydrostatic support is activated, when this state is evaluated as critical. The results show a strong increase in the load capacity and in the minimal lubrication gap of the IHPB within three series of tested experiments: stationary, nonstationary and press typical load cases.