Keywords: technological processes, mathematical modelling and simulation, experimental investigation, inverse problem, knowledge-base, staff training, e-learning.

Computer-based process analysis and process simulation have grown to high practical value and produce clear and detailed results by sophisticated algorithms and advanced graphical presentation and animation possibilities [1]. State observer models compute and visualize originally invisible inner states from measurement data. On the other hand, the numerical process model clarifies both qualitative and quantitative consequences of parameter changes and reduces the number of in-plant tests to a minimum. Analysis and discussion of the problem structure within the model replaces expensive learning by trial and error in the practice. Plants not yet built and processes not yet implemented can be proved virtually by computer-based model simulations.

But model world and industrial reality are divided by the gap of abstraction [2]: The essential factual and functional aspects of the process must be skilfully encoded in model equations and data structures. In the same way, after a model calculation the parameter sensitivity of the model results must be critically matched against technical and economical feasibilities. When the steps necessary for the solution of the technical problem are gathered by the team at last, moving into practice must be prepared carefully with information from all persons involved. This is done by periodic project meetings of the persons responsible and subsequent on-the-job training of the operators.

In order to be successful on this multistage course, responsible engineers should get consultation from one source. This requires substantial experience of the consultant just as his ability and willingness to contribute to data acquisition and to investigative process analysis, to recording of measuring data at the process and to its processing and interpretation, to modelling, model parameterization and simulation. Ideas gained with the model are then explained in the light of the model premises accepted. Decisions are to be prepared by pointing out the possibilities to act. Finally, the conclusions are to be imparted to less expert employees in the company team. The realization of this general concept is the subject of the present contribution. For the application example of continuous metal solidification processes the special software ContiSimRis used [3].

E-learning generally is defined a superior term for software-aided learning [4]. It puts the idea of 'flexible learning' into practice, providing just-in-time learning at the work place but being also relatively independent of time and place as well as of the person. This definition already explains, why the requirements for e-learning documents are higher than for a pure collection of training material and program documentation in electronic form, equipped with an on-line help and complemented by the user-friendliness of the simulation program used within the framework of the e-learning. Nevertheless, the specified elements are important pre-conditions in an e-learning-concept and are systematically made available with further use in mind.

Mathematical process modelling and simulation no longer sit within research and development departments but now work hand in hand with responsible process engineers on site. For information and communication reasons, but also with regard to limited resources, today an integrated service and consultation concept is expected, that covers expertise in the fields of measurement technology, modelling or simulation and training. Parallel to process-related objectives there is the desire to inform well-trained staff about the latest state of the development to guarantee effective work with high-quality results. This added value, achieved by skilful organizational measures is cost-effective, supports motivation, provides significant competitive advantage, and adds to site protection.

1

J.R. Boehmer, "Mesh-Independent Visualisation of Hidden 3D Process Data in Computer Simulation", Developments in Analysis and Design Using Finite Element Methods, eds. Topping, B.H.V., Kumar, B., Civil-Comp Press, Edinburgh, 13-23, 1999. doi:10.4203/ccp.59.1.3

2

J.R. Boehmer, "Methodik computergestützter Prozessmodellierung", R. Oldenbourg Verlag, München, 1997.

3

J.R. Boehmer, "Möglichkeiten und Grenzen der Optimierung des Stranggießens von NE-Metallen durch Anwendung von Modellierungswerkzeugen", Metall, 58(3), 125-133, 2004.

4

P. Baumgartner, H. Häfele, K. Maier-Häfele, "E-Learning Praxishandbuch", Studienverlag, Innsbruck, 2002.

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