Relevant bibliographies by topics / Hardenability

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Academic literature on the topic 'Hardenability'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 July 2024

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Journal articles on the topic "Hardenability"

1

Yu, Zhi Gang, Li Na Wang, and Jia Liu. "A General Steel Hardenability Calculation Method." Advanced Materials Research 816-817 (September 2013): 140–43. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.140.

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This paper presents a general calculation method of steel hardenability. First use non-linear fitting method to establish a general relationship between hardenability coefficient and end-quench distance, and then use SVM method to establish the relationship between alloying elements and hardenability coefficients. It solves the limited applicability and poor precisions problems of the currently applied calculation methods for hardenability. It gives an enhancement scheme to make sure the accuracy of the model when the data are not complete enough. Experimental data show that using this method can effectively improve the hardenability prediction accuracy and can be widely used.
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2

Smoljan, B., Dario Iljkic, N. Tomasic, Imre Felde, G. E. Totten, and Tamás Réti. "Evaluation of Steel Hardenability by JM®-Test." Materials Science Forum 537-538 (February 2007): 607–14. http://dx.doi.org/10.4028/www.scientific.net/msf.537-538.607.

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The modified Jominy-test was designed for prediction of hardenability of high-hardenability tool steels and possibility of application of modified Jominy-test in computer simulation of quenching of high-hardenability tool steels has been investigated. Because of high hardenability there are limits in application of original Jominy-specimen in simulation of quenching of steels. The performance of investigated modified Jominy-test in simulation of quenching of high-hardenability tool steels was estimated by comparison of cooling curves of modified Jominy-specimen (JM®-specimen) and cylindrical specimen. The influence of dimension of JM®-specimen on cooling curves has been investigated. The time of cooling, t8/5 relevant for results of quenching was predicted. Modified Jominy-test can be applied in simulation of quenching of steel with higher hardenability rather than original Jominy-test.
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3

Gao, Chang, Xin Liu, Dong Zhao, et al. "Recent Progress in Testing and Characterization of Hardenability of Aluminum Alloys: A Review." Materials 16, no. 13 (2023): 4736. http://dx.doi.org/10.3390/ma16134736.

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In this paper, the progress of the test methods and characterization approaches of aluminum alloys hardenability was reviewed in detail. The test method mainly included the traditional end-quenching method and the modified method. While the characterization approaches of alloy hardenability consist mainly of ageing hardness curves, solid solution conductivity curves, ageing tensile curves, time temperature transformation (TTT) curves, time temperature properties (TTP) curves, continuous cooling transformation (CCT) curves, and advanced theoretical derivation method have appeared in recent years. The hardenability testing equipment for different tested samples with different material natures, engineering applications properties, and measurement sizes was introduced. Meanwhile, the improvement programmed proposed for shortcomings in the traditional hardenability testing process and the current deficiencies during the overall hardenability testing process were also presented. In addition, the influence factors from the view of composition design applied to the hardenability behaviors of Aluminum alloys were summarized. Among them, the combined addition of micro-alloying elements is considered to be a better method for improving the hardenability of high-strength aluminum alloys.
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4

Geng, Xiaoxiao, Shuize Wang, Asad Ullah, Guilin Wu, and Hao Wang. "Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model." Materials 15, no. 9 (2022): 3127. http://dx.doi.org/10.3390/ma15093127.

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Hardenability is one of the most basic criteria influencing the formulation of the heat treatment process and steel selection. Therefore, it is of great engineering value to calculate the hardenability curves rapidly and accurately without resorting to any laborious and costly experiments. However, generating a high-precision computational model for steels with different hardenability remains a challenge. In this study, a combined machine learning (CML) model including k-nearest neighbor and random forest is established to predict the hardenability curves of non-boron steels solely on the basis of chemical compositions: (i) random forest is first applied to classify steel into low- and high-hardenability steel; (ii) k-nearest neighbor and random forest models are then developed to predict the hardenability of low- and high-hardenability steel. Model validation is carried out by calculating and comparing the hardenability curves of five steels using different models. The results reveal that the CML model works well for its distinguished prediction performance with precise classification accuracy (100%), high correlation coefficient (≥0.981), and low mean absolute errors (≤3.6 HRC) and root-mean-square errors (≤3.9 HRC); it performs better than JMatPro and empirical formulas including the ideal critical diameter method and modified nonlinear equation. Therefore, this study demonstrates that the CML model combining material informatics and data-driven machine learning can rapidly and efficiently predict the hardenability curves of non-boron steel, with high prediction accuracy and a wide application range. It can guide process design and machine part selection, reducing the cost of trial and error and accelerating the development of new materials.
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5

Li, Dongyuan, Shupeng Song, Wenbin Xia, Yong Zhou, Ze Lu, and Run Wu. "Study on the effect of microstructure change of 20CrNiV5 high strength and toughness steel on hardenability." Journal of Physics: Conference Series 2720, no. 1 (2024): 012041. http://dx.doi.org/10.1088/1742-6596/2720/1/012041.

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Abstract High-pressure and high-load drilling environment in the deep soil requires high strength and toughness of the oil well pipe, so the oil well pipe steel must be heat-treated, and the hardenability of the steel is particularly important in manufacturing. In this paper, Gleeble-3500 thermal simulator is used to measure the CCT curves of 20CrNiV5 steel, and the influence of microstructure on hardenability is studied. Deformation inhibits the martensitic transformation of the steel and leads to the decrease of its hardenability; the inhomogeneity of the steel pipe after hot forming also has an important impact on the hardenability, and the multiphase microstructure of bainite and ferrite after hot rolling leads to austenitic heating. Austenite grains are small and uneven, which reduces the hardenability of the steel. The microstructure of the inner and outer layers of the hot-formed steel pipe is smaller than that of the middle layer. After heat treatment, its smaller austenite results in weak hardenability.
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6

Gong, Wei, Zhou Hua Jiang, and Dong Ping Zhan. "A Calculation Model of Gear Steel Hardenability Modeling of Nonlinear Equation." Advanced Materials Research 233-235 (May 2011): 2352–55. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.2352.

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In the paper, the superiority-inferiority in various calculations of hardenability has been compared, and the method of nonlinear regression equation was chosen to establish a mathematical model. The model was modified through the actual production of the gear steel hardenability data. Based on the model, a prediction platform was developed with computer tools to calculate the hardenability of gear steel. The hardenability prediction software can calculate random Jominy distance hardness according to chemical composition and grain size. The forecast accuracy is better than that of unmodified nonlinear regression equation and multiple linear regression equation, but equal to that of artificial neural network.
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7

Zakharevich, S. V., and N. A. Hlazunova. "Factors influencing hardenability of steels on the example of grade 42Cr MoS4. Convergence of the results of calculation and experimental methods." Litiyo i Metallurgiya (FOUNDRY PRODUCTION AND METALLURGY), no. 1 (April 10, 2019): 66–69. http://dx.doi.org/10.21122/1683-6065-2019-1-66-69.

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This article identifies factors influencing hardenability of steels in manufacturing conditions of OJSC « BSW – Management Company of Holding «BMC». The article contains results of a study on dependence of hardenability of steel grade 42CrMoS4 on design features of the equipment and the process aimed to ensure correspondence of the fi al product to the requirements. Measures that minimize spread of values due to preparation of samples for hardenability test in industrial conditions are presented. When predicting hardenability band, it is necessary implement a package of application programs that consider influence of factors existing in the conditions of a particular steel manufacturer.
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8

Sitek, Wojciech, Jacek Trzaska, and Leszek Adam Dobrzański. "Modified Tartagli Method for Calculation of Jominy Hardenability Curve." Materials Science Forum 575-578 (April 2008): 892–97. http://dx.doi.org/10.4028/www.scientific.net/msf.575-578.892.

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Basing on the experimental results of the hardenability investigations, which employed Jominy method, the model of the neural networks was developed and fully verified experimentally. The model makes it possible to obtain Jominy hardenability curves basing on the steel chemical composition. The modified hardenability curves calculation method is presented in the paper, initially developed by Tartaglia, Eldis, and Geissler, later extended by T. Inoue. The method makes use of the similarity of the Jominy curve to the hyperbolic secant function. The empirical formulae proposed by the authors make calculation of the hardenability curve possible basing on the chemical composition of the steel. However, regression coefficients characteristic for the particular steel grade must be known. Replacing some formulae by the neural network models is proposed in the paper.
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9

Sitek, W., and A. Irla. "The Use of Fuzzy Systems for Forecasting the Hardenability of Steel." Archives of Metallurgy and Materials 61, no. 2 (2016): 797–802. http://dx.doi.org/10.1515/amm-2016-0134.

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Abstract The goal of the research carried out was to develop the fuzzy systems, allowing the determination of the Jominy hardenability curve based on the chemical composition of structural steels for quenching and tempering. Fuzzy system was created to calculate hardness of the steel, based on the alloying elements concentrations, and to forecast the hardenability curves. This was done based on information from the PN-EN 10083-3: 2008. Examples of hardenability curves calculated for exemplar steels were presented. Results of the research confirmed that fuzzy systems are a useful tool in evaluation the effect of alloying elements on the properties of materials compared to conventional methods. It has been demonstrated the practical usefulness of the developed models which allows forecasting the steels’ Jominy hardenability curve.
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10

Białobrzeska, Beata. "Effect of Alloying Additives and Microadditives on Hardenability Increase Caused by Action of Boron." Metals 11, no. 4 (2021): 589. http://dx.doi.org/10.3390/met11040589.

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The presented work was aimed at evaluating influence of boron on hardenability of steel quantitatively and evaluating this effect during complex use of boron with other alloying additives like chromium, vanadium and titanium. For this purpose, eight melts with variable chemical compositions were prepared. From the ingots, cylindrical specimens with normalized dimensions according to EN ISO 642:1999 were cut out and subjected to full annealing at 1200 °C and to normalizing at 900 °C. Such specimens were subjected to the hardenability Jominy test. In order to distinguish the influence of boron on hardenability of a given melt and thus to eliminate the differences resulting from its chemical composition, grain size and austenitizing temperature, the obtained ideal critical diameter was corrected and the boron effectiveness factor was determined. The performed examinations and analyses showed that inadequate quantities of microadditives result in losing the benefits coming from introduction of boron as the hardenability-improving element and can even result in a reduction of hardenability of the boron-containing steel.
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