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Alloy Development

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Grigoriy Zrazhevsky – One of the best experts on this subject based on the ideXlab platform.

  • Optimization of composition and processing parameters for Alloy Development: a statistical model-based approach
    Journal of Industrial & Management Optimization, 2007
    Co-Authors: Alexandr Golodnikov, Stan Uryasev, Grigoriy Zrazhevsky, Yevgeny Macheret, A. Alexandre Trindade

    Abstract:

    We describe the second step in a two-step approach for the Development of new and improved Alloys. The first step, proposed by Golodnikov et al [3], entails using experimental data to statistically model tensile yield strength and the 20th percentile of the impact toughness, as a function of Alloy composition and processing variables. We demonstrate how the models can be used in the second step to search for combinations of the variables in small neighborhoods of the data space, that result in Alloys having optimal levels of the properties modeled. The optimization is performed via the efficient frontier methodology. Such an approach, based on validated statistical models, can lead to a substantial
    reduction in the experimental effort and cost associated with Alloy Development. The procedure can also be used at various stages of the experimental program, to indicate what changes should be made in the composition and processing variables in order to shift the Alloy Development process toward the efficient frontier. Data from these more refined experiments can then be used to adjust the model and improve the second step, in an iterative search for superior Alloys.

  • Statistical modelling of composition and processing parameters for Alloy Development
    Modelling and Simulation in Materials Science and Engineering, 2005
    Co-Authors: Alexandr Golodnikov, A. Alexandre Trindade, Stan Uryasev, Yevgeny Macheret, Grigoriy Zrazhevsky

    Abstract:

    We propose the use of regression models as a tool to reduce time and cost associated with the Development and selection of new metallic Alloys. A multiple regression model is developed which can accurately predict tensile yield strength of high strength low Alloy steel based on its chemical composition and processing parameters. Quantile regression is used to model the fracture toughness response as measured by Charpy V-Notch (CVN) values, which exhibits substantial variability and is therefore not usefully modelled via standard regression with its focus on the mean. Using Monte-Carlo simulation, we determine that the three CVN values corresponding to each steel specimen can be plausibly modelled as observations from the 20th, 50th and 80th percentiles of the CVN distribution. Separate quantile regression models fitted at each of these percentile levels prove sufficiently accurate for ranking steels and selecting the best combinations of composition and processing parameters.

Alexandr Golodnikov – One of the best experts on this subject based on the ideXlab platform.

  • Optimization of composition and processing parameters for Alloy Development: a statistical model-based approach
    Journal of Industrial & Management Optimization, 2007
    Co-Authors: Alexandr Golodnikov, Stan Uryasev, Grigoriy Zrazhevsky, Yevgeny Macheret, A. Alexandre Trindade

    Abstract:

    We describe the second step in a two-step approach for the Development of new and improved Alloys. The first step, proposed by Golodnikov et al [3], entails using experimental data to statistically model tensile yield strength and the 20th percentile of the impact toughness, as a function of Alloy composition and processing variables. We demonstrate how the models can be used in the second step to search for combinations of the variables in small neighborhoods of the data space, that result in Alloys having optimal levels of the properties modeled. The optimization is performed via the efficient frontier methodology. Such an approach, based on validated statistical models, can lead to a substantial
    reduction in the experimental effort and cost associated with Alloy Development. The procedure can also be used at various stages of the experimental program, to indicate what changes should be made in the composition and processing variables in order to shift the Alloy Development process toward the efficient frontier. Data from these more refined experiments can then be used to adjust the model and improve the second step, in an iterative search for superior Alloys.

  • Statistical modelling of composition and processing parameters for Alloy Development
    Modelling and Simulation in Materials Science and Engineering, 2005
    Co-Authors: Alexandr Golodnikov, A. Alexandre Trindade, Stan Uryasev, Yevgeny Macheret, Grigoriy Zrazhevsky

    Abstract:

    We propose the use of regression models as a tool to reduce time and cost associated with the Development and selection of new metallic Alloys. A multiple regression model is developed which can accurately predict tensile yield strength of high strength low Alloy steel based on its chemical composition and processing parameters. Quantile regression is used to model the fracture toughness response as measured by Charpy V-Notch (CVN) values, which exhibits substantial variability and is therefore not usefully modelled via standard regression with its focus on the mean. Using Monte-Carlo simulation, we determine that the three CVN values corresponding to each steel specimen can be plausibly modelled as observations from the 20th, 50th and 80th percentiles of the CVN distribution. Separate quantile regression models fitted at each of these percentile levels prove sufficiently accurate for ranking steels and selecting the best combinations of composition and processing parameters.

A. Alexandre Trindade – One of the best experts on this subject based on the ideXlab platform.

  • Optimization of composition and processing parameters for Alloy Development: a statistical model-based approach
    Journal of Industrial & Management Optimization, 2007
    Co-Authors: Alexandr Golodnikov, Stan Uryasev, Grigoriy Zrazhevsky, Yevgeny Macheret, A. Alexandre Trindade

    Abstract:

    We describe the second step in a two-step approach for the Development of new and improved Alloys. The first step, proposed by Golodnikov et al [3], entails using experimental data to statistically model tensile yield strength and the 20th percentile of the impact toughness, as a function of Alloy composition and processing variables. We demonstrate how the models can be used in the second step to search for combinations of the variables in small neighborhoods of the data space, that result in Alloys having optimal levels of the properties modeled. The optimization is performed via the efficient frontier methodology. Such an approach, based on validated statistical models, can lead to a substantial
    reduction in the experimental effort and cost associated with Alloy Development. The procedure can also be used at various stages of the experimental program, to indicate what changes should be made in the composition and processing variables in order to shift the Alloy Development process toward the efficient frontier. Data from these more refined experiments can then be used to adjust the model and improve the second step, in an iterative search for superior Alloys.

  • Statistical modelling of composition and processing parameters for Alloy Development
    Modelling and Simulation in Materials Science and Engineering, 2005
    Co-Authors: Alexandr Golodnikov, A. Alexandre Trindade, Stan Uryasev, Yevgeny Macheret, Grigoriy Zrazhevsky

    Abstract:

    We propose the use of regression models as a tool to reduce time and cost associated with the Development and selection of new metallic Alloys. A multiple regression model is developed which can accurately predict tensile yield strength of high strength low Alloy steel based on its chemical composition and processing parameters. Quantile regression is used to model the fracture toughness response as measured by Charpy V-Notch (CVN) values, which exhibits substantial variability and is therefore not usefully modelled via standard regression with its focus on the mean. Using Monte-Carlo simulation, we determine that the three CVN values corresponding to each steel specimen can be plausibly modelled as observations from the 20th, 50th and 80th percentiles of the CVN distribution. Separate quantile regression models fitted at each of these percentile levels prove sufficiently accurate for ranking steels and selecting the best combinations of composition and processing parameters.