Automatic panel benders of Salvagnini Maschinenbau GmbH enable high-quality and high-efficient production of sheet metal components. To achieve the steadily increasing requirements on precision, a model-based adaptive concept has been developed controlling the complete production process as a digital twin, from CAD data to the final sheet metal part. First, an overview of the underlying simulation models with different levels of detail is given. The models consider the elastoplastic deformation of the metal sheets as well as the elastic machine components and mechatronic models of the powertrain. Secondly, an overview is given of the adaptive production concept allowing the real-time adaption of the machine to changing process parameters like material properties. Finally, all production steps are controlled by a digital twin based on this model-based adaptive strategy. The paper demonstrates the successful transfer of scientific results to an industrial application.

Model-based control of mechatronic systems requires excellent knowledge about the physical behavior of each component. For several types of components of a system, e.g. mechanical or electrical ones, the dynamic behavior can be described by means of a mathematic model consisting of a set of differential equations, difference equations and/or algebraic constraint equations. The knowledge of a realistic mathematic model and its parameter values is essential to represent the behaviour of a mechatronic system. Frequently it is hard or impossible to obtain all required values of the model parameters from the producer, so an appropriate parameter estimation technique is required to compute missing parameters. A manifold of parameter identification techniques can be found in the literature, but their suitability depends on the mathematic model. Previous work dealt with the automatic assembly of mathematical models of serial and parallel robots with drives and controllers within the dynamic multibody simulation code HOTINT as fully-fledged mechatronic simulation. Several parameters of such robot models were identified successfully by our embedded algorithm. The present work proposes an improved version of the identification algorithm with higher performance. The quality of the identified parameter values and the computation effort are compared with another standard technique.

Due to the increase in speed and lightweight construction, modern robots vibrate significantly during motion. Thus, accurate mechanical modeling and detailed controller behavior is essential for accurate path planning and control design of robots. For the suppression of undesired vibrations detailed models are used to develop robust controllers. Least square identification methods require deep insight in the analytical equations and thus are not very suitable for identification of different highly nonlinear robot models. Recently, we presented our genetic parameter identification in Brussels, Ludwig and Gerstmayr (2011). It minimizes the error of measured and simulated quantities. Highly efficient models in the multibody system tool HOTINT lead to short computational times for various simulations with different parameters. The simulation models can easily be assembled by engineers without a detailed knowledge of the underlying multibody system. As drawback of genetic optimization, many sub-minima were detected. Many simulations were required for the determination of the global minimum. Our current approach was to extend our previous algorithm. Measured and simulated quantities are transformed into the frequency domain. In contrast to previous work, Ludwig and Gerstmayr (2013), amplitude spectra of measured and simulated quantities are smoothed prior to the L2-norm computation. The presented method is tested using small scale test problems as well as real robots. Smoothing in the frequency domain leads to a smaller number of simulations needed for obtaining higher accuracy. It turns out that the presented algorithm is more accurate and precise than a standard algorithm and reduces the computational cost.

The multibody dynamics and finite element simulation code has been developed since 1997. In the past years, more than 10 researchers have contributed to certain parts of HOTINT, such as solver, graphical user interface, element library, joint library, finite element functionality and port blocks. Currently, a script-language based version of HOTINT is freely available for download, intended for research, education and industrial applications. The main features of the current available version include objects like point mass, rigid bodies, complex point-based joints, classical mechanical joints, flexible (nonlinear) beams, port-blocks for mechatronics applications and many other features such as loads, sensors and graphical objects. HOTINT includes a 3D graphical visualization showing the results immediately during simulation, which helps to reduce modelling errors. In the present paper, we show the current state and the structure of the code. Examples should demonstrate the easiness of use of HOTINT.