The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform
Ornand Cédric - One of the best experts on this subject based on the ideXlab platform.
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Artificial neural network-based modeling for preditction of hardness of austempered ductile iron
'Springer Science and Business Media LLC', 2020Co-Authors: Savangoude Ravindra, Patra, Jagdish C., Ornand CédricAbstract:Austempered ductile iron (ADI), because of its attractive properties, for example, high tensile strength along with good ductility is widely used in automotive industries. Such properties of ADI primarily depend on two factors: addition of a delicate proportion of several chemical compositions during the production of ductile cast iron and an isothermal Heat Treatment Process, called austempering Process. The chemical compositions, depending on the austempering temperature and its time duration, interact in a complex manner that influences the microstructure of ADI, and determines its hardness and ductility. Vickers hardness number (VHN) is commonly used as a measure of the hardness of a material. In this paper, an artificial neural network (ANN)-based modeling technique is proposed to predict the VHN of ADI by taking experimental data from literature. Extensive simulations showed that the ANN-based model can predict the VHN with a maximum mean absolute error (MAPE) of 0.22%, considering seven chemical compositions, in contrast to 0.71% reported in the recent paper considering only two chemical compositions
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Prediction of hardness of austempered ductile iron using enhanced multilayer perceptron based on Chebyshev expansion
'Springer Science and Business Media LLC', 2020Co-Authors: Savangoude Ravindra, Patra, Jagdish C., Ornand CédricAbstract:In various industries, e.g., manufacturing, railways, and automotive, austempered ductile iron (ADI), is extensively used because of its desirable characteristics for example, high tensile strength with good ductility. The hardness and ductility of ADI can be tailor-made for a specific application by following an appropriate Process. Such characteristics can be achieved by (i) adding a delicate proportion of several chemical compositions during the production of ductile cast iron and then followed by (ii) an isothermal Heat Treatment Process, called austempering Process. The chemical compositions, depending on the austempering temperature and its time duration, interact in a complex manner that influences the microstructure of ADI, and determines its hardness and ductility. Vickers hardness number (VHN) is commonly used as a measure of the hardness of a material. In this paper, we propose a computationally efficient enhanced multilayer perceptron (eMLP)-based technique to model the austempering Process of ADI for prediction of VHN by taking experimental data reported in literature. By comparing the performance of the eMLP model with an MLP-based model, we have shown that the proposed model provides similar performance but with less computational complexity
Ramadhani Mavindra - One of the best experts on this subject based on the ideXlab platform.
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The Effect of Solution Treatment Temperature and Quenching Media Variation in Heat Treatment Process Cu-Zn-Al Shape Memory Alloys on Shape Memory Effect and Microstructures
'State University of Malang (UM)', 2020Co-Authors: Jatimurti Wika, Gayatri Monica, Ramadhani MavindraAbstract:Shape memory alloys (SMAs) are metal alloys with a reversible ability to recover their shape at a certain temperature after being deformed. This ability referred to as Shape Memory Effect (SME). The application of SMAs such as Ni-Ti and Cu-Zn-Al alloys usually used on automotive, biomedical, etc. The commonly used SMA is Ni-Ti because of its superior SME properties than Cu-Zn-Al, even though the price is quite higher. The SME of Cu-Zn-Al might be improved by increasing the presence of the martensite phase in its microstructure by Heat Treatment. The Heat Treatment Process given to Cu-21Zn-5Al alloy is a homogenizing, annealing, solution Treatment Process and quenched with brine solution and dry ice. The Heat-treated alloys then undergo several examination trough hardness tests, X-Ray Diffraction, metallography, SME test, and Differential Scanning Calorimetry to determine the SME and microstructure of Cu-21Zn-5Al. From the test results, the specimen with temperature Treatment solution of 850oC and quenched by brine solution had the highest SME value by 36.67%. All of the microstructure contained α, β, (martensite) and γ phases
Priezjev Nikolai - One of the best experts on this subject based on the ideXlab platform.
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Atomistic Modeling of Heat Treatment Processes for Tuning the Mechanical Properties of Disordered Solids
SelectedWorks, 2020Co-Authors: Priezjev NikolaiAbstract:We investigate the effect of a single Heat Treatment cycle on the potential energy states and mechanical properties of metallic glasses using molecular dynamics simulations. We consider the three-dimensional binary mixture, which was initially cooled with a computationally slow rate from the liquid state to the solid phase at a temperature well below the glass transition. It was found that a cycle of Heating and cooling can relocate the glass to either rejuvenated or relaxed states, depending on the maximum temperature and the loading period. Thus, the lowest potential energy is attained after a cycle with the maximum temperature slightly below the glass transition temperature and the effective cooling rate slower than the initial annealing rate. In contrast, the degree of rejuvenation increases when the maximum temperature becomes greater than the glass transition temperature and the loading period is sufficiently small. It was further shown that the variation of the potential energy is inversely related to the dependence of the elastic modulus and the yield stress as functions of the maximum loading temperature. In addition, the Heat Treatment Process causes subtle changes in the shape of the radial distribution function of small atoms. These results are important for optimization of thermal and mechanical Processing of metallic glasses with predetermined properties
Savangoude Ravindra - One of the best experts on this subject based on the ideXlab platform.
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Artificial neural network-based modeling for preditction of hardness of austempered ductile iron
'Springer Science and Business Media LLC', 2020Co-Authors: Savangoude Ravindra, Patra, Jagdish C., Ornand CédricAbstract:Austempered ductile iron (ADI), because of its attractive properties, for example, high tensile strength along with good ductility is widely used in automotive industries. Such properties of ADI primarily depend on two factors: addition of a delicate proportion of several chemical compositions during the production of ductile cast iron and an isothermal Heat Treatment Process, called austempering Process. The chemical compositions, depending on the austempering temperature and its time duration, interact in a complex manner that influences the microstructure of ADI, and determines its hardness and ductility. Vickers hardness number (VHN) is commonly used as a measure of the hardness of a material. In this paper, an artificial neural network (ANN)-based modeling technique is proposed to predict the VHN of ADI by taking experimental data from literature. Extensive simulations showed that the ANN-based model can predict the VHN with a maximum mean absolute error (MAPE) of 0.22%, considering seven chemical compositions, in contrast to 0.71% reported in the recent paper considering only two chemical compositions
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Prediction of hardness of austempered ductile iron using enhanced multilayer perceptron based on Chebyshev expansion
'Springer Science and Business Media LLC', 2020Co-Authors: Savangoude Ravindra, Patra, Jagdish C., Ornand CédricAbstract:In various industries, e.g., manufacturing, railways, and automotive, austempered ductile iron (ADI), is extensively used because of its desirable characteristics for example, high tensile strength with good ductility. The hardness and ductility of ADI can be tailor-made for a specific application by following an appropriate Process. Such characteristics can be achieved by (i) adding a delicate proportion of several chemical compositions during the production of ductile cast iron and then followed by (ii) an isothermal Heat Treatment Process, called austempering Process. The chemical compositions, depending on the austempering temperature and its time duration, interact in a complex manner that influences the microstructure of ADI, and determines its hardness and ductility. Vickers hardness number (VHN) is commonly used as a measure of the hardness of a material. In this paper, we propose a computationally efficient enhanced multilayer perceptron (eMLP)-based technique to model the austempering Process of ADI for prediction of VHN by taking experimental data reported in literature. By comparing the performance of the eMLP model with an MLP-based model, we have shown that the proposed model provides similar performance but with less computational complexity
Jatimurti Wika - One of the best experts on this subject based on the ideXlab platform.
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The Effect of Solution Treatment Temperature and Quenching Media Variation in Heat Treatment Process Cu-Zn-Al Shape Memory Alloys on Shape Memory Effect and Microstructures
'State University of Malang (UM)', 2020Co-Authors: Jatimurti Wika, Gayatri Monica, Ramadhani MavindraAbstract:Shape memory alloys (SMAs) are metal alloys with a reversible ability to recover their shape at a certain temperature after being deformed. This ability referred to as Shape Memory Effect (SME). The application of SMAs such as Ni-Ti and Cu-Zn-Al alloys usually used on automotive, biomedical, etc. The commonly used SMA is Ni-Ti because of its superior SME properties than Cu-Zn-Al, even though the price is quite higher. The SME of Cu-Zn-Al might be improved by increasing the presence of the martensite phase in its microstructure by Heat Treatment. The Heat Treatment Process given to Cu-21Zn-5Al alloy is a homogenizing, annealing, solution Treatment Process and quenched with brine solution and dry ice. The Heat-treated alloys then undergo several examination trough hardness tests, X-Ray Diffraction, metallography, SME test, and Differential Scanning Calorimetry to determine the SME and microstructure of Cu-21Zn-5Al. From the test results, the specimen with temperature Treatment solution of 850oC and quenched by brine solution had the highest SME value by 36.67%. All of the microstructure contained α, β, (martensite) and γ phases
