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Nassim Boudaoud - One of the best experts on this subject based on the ideXlab platform.

  • a knowledge based system for numerical design of experiments processes in mechanical engineering
    Expert Systems With Applications, 2019
    Co-Authors: Gaetan Blondet, Julien Le Duigou, Nassim Boudaoud
    Abstract:

    Abstract This paper describes a specific knowledge-based system (KBS) to assist designers in configuring numerical design of experiments (NDoE) processes efficiently. NDoE processes are applied in product design to improve the quality of product, by taking into account variabilities and uncertainties. NDoE processes are defined by various and complex methodologies to achieve several objectives, as optimization, surrogate modeling or sensitivity analysis. On the other hand, NDoE processes may demand huge computing resources to execute hundreds simulations, and also advanced expert knowledge to set the best Configuration amongst numerous possibilities. Designers aim to obtain most useful results with a minimal computational cost as soon as possible. Thus, the Configuration Step must be as fast as possible, and it must lead to an efficient combination of complex methods, algorithms and hyper-parameters, to obtain valuable information on the product. The proposed KBS and its inference engine, a bayesian network, is detailed and applied to a product developed by automotive industry. The KBS propose new efficient Configurations to achieve designers' goal. This application shorten the Configuration Step of the NDoE process, and enables designers to use more complex methods. It also allows designers to capitalize knowledge and learn from each past NDoE process.

  • a knowledge based system for numerical design of experiments
    DS 84: Proceedings of the DESIGN 2016 14th International Design Conference, 2016
    Co-Authors: Gaetan Blondet, Le J Duigou, Nassim Boudaoud
    Abstract:

    Numerical Designs of Experiments (DoE) can be used in a simulation process for optimization or metamodelling. A DoE may be costly, and methods are used to reduce its computational cost, as adaptive DoE. They are efficient but complex to be configured and controlled. The time saved by using these methods may be lost for the Configuration Step. A knowledge based-system is proposed to capitalize and reuse each DoE process Configuration. An inference methodology, combining bayesian network and artificial neural network, is proposed. This system proposes improved Configurations to the designer.

Gaetan Blondet - One of the best experts on this subject based on the ideXlab platform.

  • a knowledge based system for numerical design of experiments processes in mechanical engineering
    Expert Systems With Applications, 2019
    Co-Authors: Gaetan Blondet, Julien Le Duigou, Nassim Boudaoud
    Abstract:

    Abstract This paper describes a specific knowledge-based system (KBS) to assist designers in configuring numerical design of experiments (NDoE) processes efficiently. NDoE processes are applied in product design to improve the quality of product, by taking into account variabilities and uncertainties. NDoE processes are defined by various and complex methodologies to achieve several objectives, as optimization, surrogate modeling or sensitivity analysis. On the other hand, NDoE processes may demand huge computing resources to execute hundreds simulations, and also advanced expert knowledge to set the best Configuration amongst numerous possibilities. Designers aim to obtain most useful results with a minimal computational cost as soon as possible. Thus, the Configuration Step must be as fast as possible, and it must lead to an efficient combination of complex methods, algorithms and hyper-parameters, to obtain valuable information on the product. The proposed KBS and its inference engine, a bayesian network, is detailed and applied to a product developed by automotive industry. The KBS propose new efficient Configurations to achieve designers' goal. This application shorten the Configuration Step of the NDoE process, and enables designers to use more complex methods. It also allows designers to capitalize knowledge and learn from each past NDoE process.

  • a knowledge based system for numerical design of experiments
    DS 84: Proceedings of the DESIGN 2016 14th International Design Conference, 2016
    Co-Authors: Gaetan Blondet, Le J Duigou, Nassim Boudaoud
    Abstract:

    Numerical Designs of Experiments (DoE) can be used in a simulation process for optimization or metamodelling. A DoE may be costly, and methods are used to reduce its computational cost, as adaptive DoE. They are efficient but complex to be configured and controlled. The time saved by using these methods may be lost for the Configuration Step. A knowledge based-system is proposed to capitalize and reuse each DoE process Configuration. An inference methodology, combining bayesian network and artificial neural network, is proposed. This system proposes improved Configurations to the designer.

Michael J Scott - One of the best experts on this subject based on the ideXlab platform.

  • Product platform design through sensitivity analysis and cluster analysis
    Journal of Intelligent Manufacturing, 2007
    Co-Authors: Michael J Scott
    Abstract:

    Scale-based product platform design consists of platform Configuration to decide which variables are shared among which product variants, and selection of the optimal values for platform (shared) and non-platform variables for all product variants. The Configuration Step plays a vital role in determining two important aspects of a product family: efficiency (cost savings due to commonality) and effectiveness (capability to satisfy performance requirements). Many existing product platform design methods ignore it, assuming a given platform Configuration. Most approaches, whether or not they consider the Configuration Step, are single-platform methods, in which design variables are either shared across all product variants or not shared at all. In multiple-platform design, design variables may be shared among variants in any possible combination of subsets, offering opportunities for superior overall design but presenting a more difficult computational problem. In this work, sensitivity analysis and cluster analysis are used to improve both efficiency and effectiveness of a scale-based product family through multiple-platform product family design.

  • Product platform design through sensitivity analysis and cluster analysis
    Journal of Intelligent Manufacturing, 2007
    Co-Authors: Zhihuang Dai, Michael J Scott
    Abstract:

    Scale-based product platform design consists of platform Configuration to decide which variables are shared among which product variants, and selection of the optimal values for platform (shared) and non-platform variables for all product variants. The Configuration Step plays a vital role in determining two important aspects of a product family: efficiency (cost savings due to commonality) and effectiveness (capability to satisfy performance requirements). Many existing product platform design methods ignore it, assuming a given platform Configuration. Most approaches, whether or not they consider the Configuration Step, are single-platform methods, in which design variables are either shared across all product variants or not shared at all. In multiple-platform design, design variables may be shared among variants in any possible combination of subsets, offering opportunities for superior overall design but presenting a more difficult computational problem. In this work, sensitivity analysis and cluster analysis are used to improve both efficiency and effectiveness of a scale-based product family through multiple-platform product family design. Sensitivity analysis is performed on each design variable to help select candidate platform design variables and to provide guidance for cluster analysis. Cluster analysis, using performance loss due to commonization as the clustering criterion, is employed to determine platform Configuration. An illustrative example is used to demonstrate the merits of the proposed method, and the results are compared with existing results from the literature.

Julien Le Duigou - One of the best experts on this subject based on the ideXlab platform.

  • a knowledge based system for numerical design of experiments processes in mechanical engineering
    Expert Systems With Applications, 2019
    Co-Authors: Gaetan Blondet, Julien Le Duigou, Nassim Boudaoud
    Abstract:

    Abstract This paper describes a specific knowledge-based system (KBS) to assist designers in configuring numerical design of experiments (NDoE) processes efficiently. NDoE processes are applied in product design to improve the quality of product, by taking into account variabilities and uncertainties. NDoE processes are defined by various and complex methodologies to achieve several objectives, as optimization, surrogate modeling or sensitivity analysis. On the other hand, NDoE processes may demand huge computing resources to execute hundreds simulations, and also advanced expert knowledge to set the best Configuration amongst numerous possibilities. Designers aim to obtain most useful results with a minimal computational cost as soon as possible. Thus, the Configuration Step must be as fast as possible, and it must lead to an efficient combination of complex methods, algorithms and hyper-parameters, to obtain valuable information on the product. The proposed KBS and its inference engine, a bayesian network, is detailed and applied to a product developed by automotive industry. The KBS propose new efficient Configurations to achieve designers' goal. This application shorten the Configuration Step of the NDoE process, and enables designers to use more complex methods. It also allows designers to capitalize knowledge and learn from each past NDoE process.

Taeman Han - One of the best experts on this subject based on the ideXlab platform.

  • ecu Configuration framework based on autosar ecu Configuration metamodel
    International Conference on Hybrid Information Technology, 2009
    Co-Authors: Joochul Lee, Taeman Han
    Abstract:

    AUTOSAR (AUTomotive Open System ARchitecture) is a partnership of automotive manufacturers and suppliers working together to develop and establish a de-facto open industry standard for automotive E/E architectures. AUTOSAR defines architecture, methodology, and application interfaces. The methodology describes ways to exchange formats or description templates to enable a seamless Configuration process of the basic software stack and the integration of application software in ECUs and it includes even the methodology how to use this framework. The Configuration process is comprised of several Steps such as system Configurations and ECU Configuration. In ECU Configuration Step, many ECU Configuration parameters based on system Configuration results are organized into XML files. This paper describes methodology how these ECU Configuration parameters are described, classified, and stored in XML files.