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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 331-338 
    ISSN: 1572-8145
    Keywords: Simulation ; modelling ; machine learning ; evolutionary algorithms ; artificial neural network
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The use of simulation technology as a tool for planning and control is of increasing significance in most fields of production. The main part of the expenditure concerning simulation analyses is the modelling of the considered production. Despite the use of modern building-block-oriented modelling technology, this modelling can often not be done by the user, but only by external experts. Against this backdrop, an adaptive simulation system is being developed by the Institute for Industrial Manufacturing and Management (IFF) at the University of Stuttgart. It independently adapts to real production processes, i.e. it learns about the interdependencies of production processes, and, in this way, supports the user in constructing and maintaining the model. In terms of information technology, the research in the field of artificial intelligence, especially in the subdomain of machine learning, is the basis for the realization of such adaptive systems.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 289-294 
    ISSN: 1572-8145
    Keywords: Manufacturing process chain ; modelling ; optimization ; neural networks ; evolutionary algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Today's manufacturing methods are caught between the growing need for quality, high process safety, minimal manufacturing costs, and short manufacturing times. In order to meet these demands, process setting parameters have to be chosen in the best possible way, according to demand on quality. For such optimization it is necessary to represent the processes in a model. Due to the enormous complexity of many processes and the high number of influencing parameters, however, conventional approaches to modelling and optimization are no longer sufficient. In this article it is shown how, by means of applying neural networks for process modelling, even these highly complex interdependencies can be learned. That way both process and quality parameters can be assessed before or during processing. By connecting them with corresponding cost models, it is possible to optimize processes with the help of evolutionary algorithms. Using examples of different manufacturing processes, the possi bilities for process modelling and optimization with neural networks and evolutionary algorithms are demonstrated.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
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