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@PHDTHESIS{Mller:783185,
author = {Müller, Mario},
othercontributors = {Hüsing, Mathias and Corves, Burkhard and Modler,
Karl-Heinz},
title = {{K}ünstliche {I}ntelligenz zur {S}truktur- und
{M}aßsynthese ebener {F}ührungs- und
Übertragungsgetriebe},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2020-02351},
pages = {1 Online-Ressource (xi, 127, XXXIV Seiten) :
Illustrationen, Diagramme},
year = {2020},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2020},
abstract = {Mechanisms are mechanical solutions for the transformation
of motion or for the transmission of forces and torques. The
increasing requirements on mechanisms in practice can hardly
be met by a developer without in-depth knowledge in
mechanism theory. Therefore, the research field of
artificial intelligence is used to support the development
process. It provides solutions to help both experts and
nonprofessionals to solve complex problems. Such IT systems
are known in engineering and science as expert systems.
Within this work, an artificial intelligence was developed
in the form of an expert system for mechanism synthesis.
Starting from a motion task, the artificial intelligence can
autonomously go through the mechanism design process. The
motion task is thereby independent from potential synthesis
methods, and can be formulated for both transfer and
guidance mechanisms. Just as in classical mechanism
synthesis, the artificial intelligence addresses the two
topics of structural and dimensional synthesis. In the
subject area of the structural synthesis, potentially
suitable mechanism structures are determined for the
following step of the dimensional synthesis. Therefore, in
the context of this thesis, a database has been developed
which contains mechanism structures for the fulfillment of
transfer and guidance mechanisms. To generate such mechanism
structures, a graph-based algorithm has been developed. The
structures identified by this algorithm can consist of
revolute joints, prismatic joints as well as cam joints. Due
to the specific properties of the mechanism structures, the
number of potentially suitable structures can be drastically
limited within the structural synthesis by means of the set
of rules of artificial intelligence. These mechanism
structures can then be dimensioned within the dimensional
synthesis by using optimization-based algorithms. For this,
they are divided into recurring modules. Because of this
procedure, solutions for complex mechanism structures can be
determined with comparatively few dimensioning algorithms.
The validation of the developed artificial intelligence
shows that it is not only able to find known solutions of a
problem, but also to extend the known scope of solution by
further mechanisms. Thus, the artificial intelligence does
not only provide a support for non-professionals but also
for experts.},
cin = {411910},
ddc = {620},
cid = {$I:(DE-82)411910_20180101$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2020-02351},
url = {https://publications.rwth-aachen.de/record/783185},
}
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