Vickers

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Ali Nazari - One of the best experts on this subject based on the ideXlab platform.

  • Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels
    Neural Computing and Applications, 2013
    Co-Authors: Gholamreza Khalaj, Ali Reza Khodabandeh, Hossein Yoozbashizadeh, Ali Nazari
    Abstract:

    In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900–1,100°C) and subsequent cooling rate (0.15–227°C/s) and initial austenite grain size (5–130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.

  • retraction note to artificial neural network to predict the effect of heat treatments on Vickers microhardness of low carbon nb microalloyed steels
    Neural Computing and Applications, 2013
    Co-Authors: Gholamreza Khalaj, Ali Reza Khodabandeh, Hossein Yoozbashizadeh, Ali Nazari
    Abstract:

    In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900–1,100°C) and subsequent cooling rate (0.15–227°C/s) and initial austenite grain size (5–130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.

  • application of artificial neural networks for analytical modeling of charpy impact energy of functionally graded steels
    Neural Computing and Applications, 2013
    Co-Authors: Ali Nazari
    Abstract:

    In the present study, the Charpy impact energy of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled in crack divider configuration. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using, respectively, 174 and 120 experimental data were conducted. According to the input parameters, in the neural networks model, the Vickers microhardness of each layer was predicted. A good fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the yield stress of the corresponding layer and by assuming Holloman relation for stress–strain curve of each layer, the area under each stress–strain curve was acquired. Finally, by applying the rule of mixtures, Charpy impact energy of functionally graded steels in crack divider configuration was found through numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.

  • microhardness profile prediction of a graded steel by strain gradient plasticity theory
    Computational Materials Science, 2011
    Co-Authors: Ali Nazari, Jamshid Aghazadeh Mohandesi, Saeed Tavareh
    Abstract:

    In the present study, the Vickers microhardness profile of functionally graded steel austenitic steel produced by electroslag remelting process has been investigated. To produce functionally graded steels, two different slices from plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with mechanism-based strain gradient plasticity theory. In this regard, the density of the statistically stored dislocations and that of geometrically necessary dislocations was related to the Vickers microhardness profile of each layer. The experimental results are in good agreement with those obtained from the theory.

Dongil Kwon - One of the best experts on this subject based on the ideXlab platform.

Seungkyun Kang - One of the best experts on this subject based on the ideXlab platform.

R Caram - One of the best experts on this subject based on the ideXlab platform.

Richard C Bradt - One of the best experts on this subject based on the ideXlab platform.

  • on the Vickers indentation fracture toughness test
    Journal of the American Ceramic Society, 2007
    Co-Authors: George D Quinn, Richard C Bradt
    Abstract:

    The Vickers indentation fracture toughness test, or VIF, is addressed by considering its origins and the numerous equations that have been applied along with the technique to estimate the fracture resistance, or the KIc of ceramics. Initiation and propagation of cracks during the VIF test are described and contrasted with the pre-cracking and crack growth for internationally standardized fracture toughness tests. It is concluded that the VIF test technique is fundamentally different than standard fracture toughness tests. The VIF test has a complex three-dimensional crack system with substantial deformation residual stresses and damage around the cracks. The VIF test relates to an ill-defined crack arrest condition as opposed to the rapid crack propagation of the standardized fracture toughness tests. Previously published fracture toughness results employing the VIF technique are reviewed. These reveal serious discrepancies in reported VIF fracture toughness values. Finally, recent fracture resistance measurements by the VIF technique for the Standard Reference Material SRM 2100 are presented. These are compared with standardized test results for the same material. It is concluded that the VIF technique is not reliable as a fracture toughness test for ceramics or for other brittle materials. What the VIF actually measures in terms of fracture resistance cannot be readily defined. It is recommended that the VIF technique no longer be acceptable for the fracture toughness testing of ceramic materials.