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Machine tools are important parts of high-complex industrial manufacturing. Thus, the end product quality strictly depends on the accuracy of these machines, but they are prone to deformation caused by their own heat. The deformation needs to be compensated in order to assure accurate production. So an adequate model of the high-dimensional thermal deformation process must be created and parameters of this model must be evaluated. Unfortunately, such parameters are often unknown and cannot be calculated a priori. Parameter identification during real experiments is not an option for these models because of its high engineering and machine time effort. The installation of additional sensors to measure these parameters directly is uneconomical. Instead, an effective calibration of thermal models can be reached by combining real and virtual measurements on a machine tool during its real operation, without additional sensors installation. In this paper, a new approach for thermal model calibration is presented. The expected results are very promising and can be recommended as an effective solution for this class of problems.

Process identification, i.e. finding static, dynamic or signal models, requires good knowledge in the application of statistical analysis, signal and system theory. The main goal of this article is to prepare the process identification technology for the process analyst in a clear and understandable way. A new philosophy of usability, process analysis, visualization, consulting and memory function at the example of the ADM tool, which shows a method-integrated approach, is introduced.

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