Prediction of electrical resistivity of steel using artificial neural network

Koley, Soumyajit; Ray, Trishita; Mohanty, Itishree; Chatterjee, Soumya; Shome, Mahadev

doi:10.1080/03019233.2017.1403109

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…However, it is to be mentioned that calculations showed that the M s and M f temperatures did not vary significantly with T γ suggesting that PAGS has limited influence on the M s and M f temperatures. 45 From Table 2, it can be observed that the calculated transformation temperatures are lower than the experimentally determined values. This discrepancy is explained based on an assumption of a homogeneous austenite with the given composition of the steel, while actually the incomplete dissolution of carbides leads to an inhomogeneous austenite, especially with increase in W concentration.…”

Section: Resultsmentioning

confidence: 79%

Decomposition modes of austenite in 9Cr–W–V–Ta reduced activation ferritic–martensitic steels

Ravikirana

Mythili

Raju

et al. 2015

Materials Science and Technology

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The present paper presents the results of an extensive electron microscopy investigation on the decomposition modes of high temperature austenite in 9Cr–W–V–Ta reduced activation ferritic–martensitic steels. Although the displacive martensitic transformation is predominant on austenitisation, low volume fraction of Fe rich M3C or M23C6 precipitates formed, when the tungsten content exceeded 1 wt-. The compositional inhomogeneity introduced in the austenite by the nature, chemistry and kinetics of dissolution of the pre-existing carbides is dependent on the steel composition and austenitisation conditions. The extent of repartitioning of tungsten between M23C6 and ferrite largely influences the kinetics of austenite and martensite transformation, for the same austenitisation conditions. Supporting evidence from calorimetry analysis is also presented.

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Section: Resultsmentioning

confidence: 79%

Decomposition modes of austenite in 9Cr–W–V–Ta reduced activation ferritic–martensitic steels

Ravikirana

Mythili

Raju

et al. 2015

Materials Science and Technology

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“…Decision trees are a popular supervised learning method that, like many other learning methods, can be used for both regression and classification [42][43]. The working principle of decision trees is to split the data into subsets, where each subgroup belongs to only one class.…”

Section: Decision Treementioning

confidence: 99%

Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework

Choudhury

2021

Preprint

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Prediction of process route from materials with a desired set of property is one of the fundamental issues in the perspective of the materials design aspect. The parameter space is often too large to be bound since there are too many possibilities. However, in the areas with limited theoretical access, artificial learning techniques can be attempted by using the available data of the candidate materials. In this study, a computational method has been proposed to predict the different process routes of steel constituted taking composition and desired mechanical properties as inputs. First of all, historical data of the actual rolling process was collected, cleaned, and integrated. Further, the dataset is divided into four different classes based on the rolling process data. Then, to find out the essential characteristics of variables, feature correlation among various features has been calculated. A state-of-art machine learning prediction methods such as logistic regression, K- nearest neighbor, Support vector machine, and the random forest are studied to implement the prediction model. In order to avoid the overfitting of the model, k fold cross-validation is applied to the model, and achieve a realistic prediction result with an accuracy of 97%. The F1-score of the classification model is 0.86, and the kappa score is 0.95, which comply that the model has excellent learning and speculation ability and the precise forecast of steel process routes based on the given input parameters.

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“…The electrical resistance measurements have recently been reexplored to indirectly or directly identify material properties [8][9][10][11]. For example, Koley et al in [12], have reported an electrical resistance model estimator based on a neural network. The model predicts the electrical resistivity from the chemical composition of the steel.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Steel Electrical-Resistance Kinetics during High-Temperature Experiments using an Embedded System

Chávez-Campos

Campos

Vergara–Hernández

et al. 2023

Preprint

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Metals’ properties can be enhanced through high-temperature treatments. These treatments can also enhance the mechanical properties of metals, such as strength, hardness, and ductility. This is achieved by altering the microstructure of the metal, which can improve its ability to withstand stress and deformation. To ensure the effectiveness of the processes, samples undergo mechanical and optical characterization. During high-temperature treatments, oxidation kinetics is often studied through controlled experiments. However, traditional methods involve a complicated, time-consuming, and iterative process to track oxidation changes at different intervals, leading to limited data. This paper proposes the use of metals’ electrical resistance as a method to monitor oxidation kinetics during high-temperature experiments with AISI-304. An embedded system has been developed to control and record pertinent experimental data. The four-point method, backed by the Ordinary Least-Square Estimator, is employed to measure electrical resistance. Data analysis shows an increase in resistance value, which is attributed to the loss of mass/area caused by high-temperature oxidation.

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Prediction of electrical resistivity of steel using artificial neural network

Cited by 12 publications

References 21 publications

Decomposition modes of austenite in 9Cr–W–V–Ta reduced activation ferritic–martensitic steels

Decomposition modes of austenite in 9Cr–W–V–Ta reduced activation ferritic–martensitic steels

Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework

Analysis of Steel Electrical-Resistance Kinetics during High-Temperature Experiments using an Embedded System

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