Information management system to support technology start-up and technology knowledge formation (Tec-In)

 Important data at a glance

© BMWi / Dresden exists

Objective

In "Tec-In", the focus was on the development of an "information management system to support technology start-ups and the formation of technology knowledge". This was initially based on the methodological results for modeling and analyzing technological process chains that were developed in Collaborative Research Center 639 "Textile-reinforced composite components for function-integrating mixed construction methods in complex lightweight applications" and in the Saxon Leading-Edge Cluster "European Centre for Emerging Materials and Processes Dresden".

In Tec-In, the following sub-objectives were pursued:

• For the application of the methods for the systematic description and analysis of technological process chains for the support of technology commissioning, further methods were aimed at, which support the organizational processes and the information flows during commissioning. The marketability of the product to be developed was to be established.
• To illustrate the methods, a demonstration concept was to be developed in which selected methods were to be implemented in a prototypical IT support. The demonstrator serves to support the necessary marketing measures with regard to the acquisition of industrial projects.
• For the marketing of the product suitable enterprise concepts were to be compiled and a company was to be created.

Solution

Regarding the points:

• The development of methods for start-up began with the analysis of the data-driven approach for the description and analysis of technological process chains with regard to practical application in the industrial environment. According to the analysis results, methods were developed for clear and structured description of the objective of process chain analysis and improvement, for systematic user guidance in process description, for inclusion of existing expertise, for preparation of data collection and analysis. – The additional methods are mostly based on techniques for facilitation (questioning of experts) and for evaluation and structuring of information.
• As a demonstrator, a concept was selected that is deliberately decoupled from the production-related content, but can still illustrate the principle procedure. A process chain suitable for the explanations was developed for the demonstrator. For selected methods, prototypical IT functions were developed and tested in a field trial.
• In cooperation with Dresden exists and other partners, necessary strategies for market presence, pricing and PR work had to be created. In addition, a sustainable business plan had to be developed and start-up support measures had to be implemented.

Results

Regarding the points:

• From a methodological point of view, two main procedures were developed. For dealing with production engineering issues, a process was developed from the discussion of the problem to the application of analysis results (see Fig. 1). For dealing with interested companies, a phase model for project implementation was developed, which ultimately makes projects more transparent and billable.
• The modeling of the preparation process, the experimental planning for a start-up, the data acquisition and analysis, and the development of a data-based control model for influencing the process were implemented on a high-quality, controllable fully automatic coffee machine. The demonstrator has since been successfully used at several trade shows.
• The developed strategy led to pilot projects with manufacturing companies in different industries (e.g. injection molding, machining, laser processing). The business plan was reviewed by the project sponsor and recommended for further funding. In April 2012, Symate GmbH was spun off. Since then, Symate has been developing and marketing the IIoT platform Detact. Detact is a fully automated smart factory tool and AI control center for digitizing their production (Fig. 2). Data can be analyzed using methods from statistics to AI. Results can be complex data-based control models for process control (Fig. 3).

© Detact

© Detact

Publications

Großmann, K. ; Wiemer, H. ; Dietz, G. : Unterstützung der Entwicklung innovativer Technologien – Systematische Ermittlung von Prozessfenstern. In: ZWF 107 (2012), Nr. 11, S. 808–813

Wiemer, H. ; Dietz, G. ; Juhrisch, M. ; Weller, J. : Mehr als nur Statistik – Symate GmbH bietet Prozessoptimierung mit neuartigem Technologiedatenmanagement-System. In: DresdnerTransferbrief 1.12 (20. Jahrgang), Dresden, 2012

Wiemer, H. : Technologiedatenmanagement als Werkzeug für Technologieentwicklung und Wissenstransfer. In: Tagungsband zum Internationalen Kolloquium „Ressourcenschonende Werkstoffe – Technologien – Prozesse“ des Spitzentechnologieclusters ECEMP 2013 Dresden, Verlag wissenschaftliche Scripten, 2013

Großmann, K. ; Wiemer, H. ; Großmann, K. K. : Methods for Modelling and Analysing Process Chains for Supporting the Development of New Technologies. In: Procedia Materials Science 2 (2013), S. 34–42

Helbig, M. ; Wiemer, H. ; Weller, J. : Transferring Knowledge from Research to Industry: Experiences from Germany. In: PACIS 2015 Proceedings, Paper 25 Link

Helbig, M. ; Wiemer, H. ; Weller, J. : From research to industry: transfer of complex process knowledge. In: International Conference on Innovative Technologies In-Tech 2015, Dubrovnik, 09.-11.09.2015, Proceedings und Online-Proceedings der In-Tech 2015

Wiemer, H. ; Müller, T. ; Hauptmann, M. ; J.-P. Majschak : Effiziente Entwicklung komplexer Umformprozesse faserbasierter Materialien durch Methoden des Technologiedatenmanagements. In: Tagungsband zur 8. wissenschaftlichen Fachtagung VVD 2015 Verarbeitungsmaschinen und Verpackungstechnik, S. 301–320

Wiemer, H. ; Juhrisch, M. : Detact® – Ein smartes System für die effiziente Entwicklung von innovativen Technologien. In: Dresdner Transferbrief 3.16, Herausgeber: TU Dresden Forschungsförderung/Transfer, TechnologieZentrumDresden GmbH, Industrie- und Handelskammer Dresden GWT-TUD GmbH, Dresden, 2016, S. 10–11

Contact

© Michael Kretzschmar

Research associate

Name

Mr Dr.-Ing. Hajo Wiemer

Head of the depart. Process Informatics and Machine Data Analysis

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Chair of Machine Tools Development and Adaptive Controls

Chair of Machine Tools Development and Adaptive Controls

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