The Experts below are selected from a list of 37806 Experts worldwide ranked by ideXlab platform
Grigoriy Zrazhevsky - One of the best experts on this subject based on the ideXlab platform.
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Optimization of composition and processing parameters for Alloy Development: a statistical model-based approach
Journal of Industrial & Management Optimization, 2007Co-Authors: Alexandr Golodnikov, Stan Uryasev, Grigoriy Zrazhevsky, Yevgeny Macheret, A. Alexandre TrindadeAbstract: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.
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Statistical modelling of composition and processing parameters for Alloy Development
Modelling and Simulation in Materials Science and Engineering, 2005Co-Authors: Alexandr Golodnikov, Stan Uryasev, Yevgeny Macheret, A. Alexandre Trindade, Grigoriy ZrazhevskyAbstract: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.
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Optimization of composition and processing parameters for Alloy Development: a statistical model-based approach
Journal of Industrial & Management Optimization, 2007Co-Authors: Alexandr Golodnikov, Stan Uryasev, Grigoriy Zrazhevsky, Yevgeny Macheret, A. Alexandre TrindadeAbstract: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.
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Statistical modelling of composition and processing parameters for Alloy Development
Modelling and Simulation in Materials Science and Engineering, 2005Co-Authors: Alexandr Golodnikov, Stan Uryasev, Yevgeny Macheret, A. Alexandre Trindade, Grigoriy ZrazhevskyAbstract: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.
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Optimization of composition and processing parameters for Alloy Development: a statistical model-based approach
Journal of Industrial & Management Optimization, 2007Co-Authors: Alexandr Golodnikov, Stan Uryasev, Grigoriy Zrazhevsky, Yevgeny Macheret, A. Alexandre TrindadeAbstract: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.
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Statistical modelling of composition and processing parameters for Alloy Development
Modelling and Simulation in Materials Science and Engineering, 2005Co-Authors: Alexandr Golodnikov, Stan Uryasev, Yevgeny Macheret, A. Alexandre Trindade, Grigoriy ZrazhevskyAbstract: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.
Markus Rettenmayr - One of the best experts on this subject based on the ideXlab platform.
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Resolidification of a mushy-zone and directional solidification: a method for efficient Alloy Development demonstrated using the example of Cu-Ga-Sn.
Scientific reports, 2020Co-Authors: Martin Salge, Gunther Wiehl, Klaus Hack, Markus RettenmayrAbstract:An experimental method for Alloy Development that allows to systematically scan multicomponent Alloy systems is presented using the Cu-Ga-Sn system as an example. Rods with homogeneous concentration distribution of different initial compositions are annealed in a steep temperature gradient with temperatures in the range from above liquidus to below solidus temperature. During resolidification of the initially formed mushy-zone, a continuously varying composition over the length of the rods develops. Further concentration gradients of the Alloying elements are generated during subsequent directional solidification. The graded samples are evaluated for different properties. Vickers hardness as a function of composition was measured along the length of the samples to get first information on the mechanical behavior of bulk samples. The melting range of selected compositions (cylindrical disks of 1 mm thickness cut out of the rods) was determined by differential scanning calorimetry and compared to liquidus temperatures extrapolated from the binary systems with a fitting method and the Calphad method. With the procedure introduced here, it is possible to determine several Alloy properties over an extended composition range of a multicomponent system with significantly reduced experimental effort.
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Alloy Development using modern tools
International Journal of Materials Research, 2009Co-Authors: Markus RettenmayrAbstract:The Development of new Alloys has always been a prominent field in materials science. For modern materials, the conditions that need to be considered are manifold. The properties of a material depend firstly on its composition, but also on the processing during its production and on the initial microstructure. In view of the complexity of modern materials and the numerous possibilities to influence their properties it is necessary to use a variety of tools for their optimization. Theoretical and empirical approaches should be combined in order to achieve a goal in a reasonable amount of time. On the theoretical side there is the calculation of type and amount of phases considering thermodynamic equilibrium, or in more sophisticated cases the combination of thermodynamic and kinetic calculations. If the necessary data are not available, some entities can also be estimated. On the experimental side all of the available methods for producing and analyzing metal Alloys should be applied wherever necessary. The goal is a more profound understanding of microstructural evolution so that it can be influenced specifically and Alloy candidates are not excluded prematurely if the properties of an Alloy are not satisfactory after a first unsuccessful attempt. Examples that are presented here are Development steps for Pb-free solder Alloys (Bi and Zn based Alloys) and the interrelation of Alloy and processing Development for an Al-Fe-Si Alloy.
Stan Uryasev - One of the best experts on this subject based on the ideXlab platform.
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Optimization of composition and processing parameters for Alloy Development: a statistical model-based approach
Journal of Industrial & Management Optimization, 2007Co-Authors: Alexandr Golodnikov, Stan Uryasev, Grigoriy Zrazhevsky, Yevgeny Macheret, A. Alexandre TrindadeAbstract: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.
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Statistical modelling of composition and processing parameters for Alloy Development
Modelling and Simulation in Materials Science and Engineering, 2005Co-Authors: Alexandr Golodnikov, Stan Uryasev, Yevgeny Macheret, A. Alexandre Trindade, Grigoriy ZrazhevskyAbstract: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.
