The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform

Panagiotis Giannopoulos - One of the best experts on this subject based on the ideXlab platform.

Roman Slivin - One of the best experts on this subject based on the ideXlab platform.

  • Intelligent support of the preprocessing stage of Engineering Analysis using case-based reasoning
    Engineering with Computers, 2008
    Co-Authors: Peter Wriggers, Marina Siplivaya, Irina Joukova, Roman Slivin
    Abstract:

    The process of Engineering Analysis, especially its preprocessing stage, comprises some knowledge-based tasks which influence the quality of the results greatly, require considerable level of expertise from an engineer; the support for these tasks by the contemporary CAE systems is limited. Analysis of the knowledge and reasoning involved in solving these tasks shows that the appropriate support for them by an automated system can be implemented using case-based reasoning (CBR) technology. In this paper the automated knowledge-based system for intelligent support of the preprocessing stage of Engineering Analysis in the contact mechanics domain is presented which employs the CBR mechanism. The case representation model is proposed which is centered on the structured qualitative model of a technical object. The model is formally represented by the Ontology Web Language Description Logics (OWL DL) ontology. Case retrieval and adaptation algorithms for this model are described which according to the initial tests perform better in the chosen domain then the known prototypes. The automated system is described and a sample problem-solving scenario from the contact mechanics domain is presented. Use of such system can potentially lower costs of Engineering Analysis by reducing the number of inappropriate decisions and Analysis iterations and facilitate knowledge transfer from research into industry.

  • Intelligent support of Engineering Analysis using ontology and case-based reasoning
    Engineering Applications of Artificial Intelligence, 2007
    Co-Authors: Peter Wriggers, Marina Siplivaya, Irina Joukova, Roman Slivin
    Abstract:

    Accuracy and reliability of the FEM Analysis results depend heavily on the quality of the decisions made during the Analysis process. As there are no industry-level systems for support of non-algorithmic tasks of FEM-based Engineering Analysis, such tasks are carried out by engineers on the basis of expert knowledge and experience. However, to exploit contemporary potentialities of FEM to solve a complex Engineering problem requires high level of expertise; this restricts application of achievements of FE Analysis in industry. In this paper, the concept of intelligent support of Engineering Analysis using knowledge-based system is presented, which is a promising way to increase quality of complex Analysis.

Arthi Jayaraman - One of the best experts on this subject based on the ideXlab platform.

Peter Wriggers - One of the best experts on this subject based on the ideXlab platform.

  • Intelligent support of the preprocessing stage of Engineering Analysis using case-based reasoning
    Engineering with Computers, 2008
    Co-Authors: Peter Wriggers, Marina Siplivaya, Irina Joukova, Roman Slivin
    Abstract:

    The process of Engineering Analysis, especially its preprocessing stage, comprises some knowledge-based tasks which influence the quality of the results greatly, require considerable level of expertise from an engineer; the support for these tasks by the contemporary CAE systems is limited. Analysis of the knowledge and reasoning involved in solving these tasks shows that the appropriate support for them by an automated system can be implemented using case-based reasoning (CBR) technology. In this paper the automated knowledge-based system for intelligent support of the preprocessing stage of Engineering Analysis in the contact mechanics domain is presented which employs the CBR mechanism. The case representation model is proposed which is centered on the structured qualitative model of a technical object. The model is formally represented by the Ontology Web Language Description Logics (OWL DL) ontology. Case retrieval and adaptation algorithms for this model are described which according to the initial tests perform better in the chosen domain then the known prototypes. The automated system is described and a sample problem-solving scenario from the contact mechanics domain is presented. Use of such system can potentially lower costs of Engineering Analysis by reducing the number of inappropriate decisions and Analysis iterations and facilitate knowledge transfer from research into industry.

  • Intelligent support of Engineering Analysis using ontology and case-based reasoning
    Engineering Applications of Artificial Intelligence, 2007
    Co-Authors: Peter Wriggers, Marina Siplivaya, Irina Joukova, Roman Slivin
    Abstract:

    Accuracy and reliability of the FEM Analysis results depend heavily on the quality of the decisions made during the Analysis process. As there are no industry-level systems for support of non-algorithmic tasks of FEM-based Engineering Analysis, such tasks are carried out by engineers on the basis of expert knowledge and experience. However, to exploit contemporary potentialities of FEM to solve a complex Engineering problem requires high level of expertise; this restricts application of achievements of FE Analysis in industry. In this paper, the concept of intelligent support of Engineering Analysis using knowledge-based system is presented, which is a promising way to increase quality of complex Analysis.

Jason B. Pleming - One of the best experts on this subject based on the ideXlab platform.

  • Probabilistic Engineering Analysis using the NESSUS software
    Structural Safety, 2005
    Co-Authors: Ben H. Thacker, David S. Riha, Simeon H. K. Fitch, Luc Huyse, Jason B. Pleming
    Abstract:

    The development of reliability-based design methods requires the use of general-purpose Engineering Analysis tools that predict the uncertainty in a response due to uncertainties in the model formulation and input parameters. Barriers that have prevented the full acceptance of probabilistic Analysis methods in the Engineering design community include availability of tools, ease of use, robust and accurate probabilistic Analysis methods, and the ability to perform probabilistic analyses for large-scale problems. The goal of the reported work has been to develop a software tool that fully addresses these three aspects (availability, robustness and efficiency) to enable the designer to efficiently and accurately account for uncertainties as they might affect structural reliability and risk assessment. The paper discusses the NESSUS probabilistic Engineering Analysis software with specific sections on the reliability modeling and Analysis process in NESSUS, the robust and accurate solution strategies incorporated in the available probabilistic Analysis methods, and several application examples to demonstrate the applicability of probabilistic Analysis to large-scale Engineering problems.