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Projects / Programmes source: ARIS

Synergetics of Technological processes

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Research activity

Code Science Field Subfield
2.21.00  Engineering sciences and technologies  Technology driven physics   

Code Science Field
T000  Technological sciences   
P170  Natural sciences and mathematics  Computer science, numerical analysis, systems, control 
Keywords
technological processes, synergetics, modeling, artificial neural networks, non-linearity, chaos, pharmaceutical processes, cutting, grinding, surfaces, non-destructive tesing
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (5)
no. Code Name and surname Research area Role Period No. of publications
1.  06322  Milko Čefarin  Energy engineering  Researcher  1998 - 1999  32 
2.  00800  PhD Igor Grabec  Computer science and informatics  Head  1997 - 1999  696 
3.  18081  PhD Janez Gradišek  Mechanical design  Researcher  1999  92 
4.  09002  Peter Mužič  Manufacturing technologies and systems  Researcher  1997 - 1999  95 
5.  10425  PhD Egon Susič  Computer science and informatics  Researcher  1997 - 1999  44 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0782  University of Ljubljana, Faculty of Mechanical Engineering  Ljubljana  1627031  29,214 
Abstract
The object of the research in the project is a description of those physical properties of technological processes that are a consequence of interaction of many basic phenomena and which cause their complex character. The basis of the research are experimental explorations and a non-linear modeling of phenomena based on measured data. For the purpose of non-linear modeling the methods of the artificial neural networks. are developed and applied. The research on this field is focussed on optimization of a self-organized adaptation of the network and searching of its optimal structure. The developed neural networks are furder utilized at the modeling of fermentation processes in pharmaceutical industry, development of new methods of non-destructive testing by analysis of acoustic emission, modeling of chaotic time series and fields, and modeling of manufacturing processes. At the modeling of manufacturing processes the attention is on turning and grinding and characterization of their cahotic properties, which are the consequence of the non-linear interaction of various vibration modes of manufacturing machines. In relation to this project there are conducted the following 4 projects inside the COST P4 Action: Non-linear Dynamics in Mechanical Processing: 1. Synergetics of Technological Processes, 2. Investigations of Chaotic Properties of the Grinding Process, 3. Characterization of Technological Processes, 4. Non-linear Dynamics of Cutting Process and its Influence on Workpiece and Tool and two projects for VW Foundation in R. Germany.
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