A “new” empirical equation to describe the strain hardening behavior of steels and other metallic materials

Lavakumar, Avala; Sarangi, Soumya Sourav; Chilla, Venkat; Narsimhachary, D.; Ray, R.K.

doi:10.1016/j.msea.2020.140641

Cited by 18 publications

(16 citation statements)

References 37 publications

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“…A modified version of Equation ( 6 ), usually accepted for austenitic stainless steels and in the presence of carbide precipitates [ 41 ], was proposed by Ludwigson [ 43 ]:

where

, and

are material parameters. Modified versions of the Hollomon and Ludwigson equations [ 27 ] have also been proposed for metals with different carbon contents. In fact, carbon content is known to be one of the primary factors influencing the strain hardening [ 27 , 44 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Modified versions of the Hollomon and Ludwigson equations [ 27 ] have also been proposed for metals with different carbon contents. In fact, carbon content is known to be one of the primary factors influencing the strain hardening [ 27 , 44 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…However, describing the stress response to strain deformation over the different stages of the tensile test and for a whole range of materials with a single expression is hardly possible [ 27 , 47 ]. In this study, the focus is not only on the identification of a good model for describing the whole range stress–strain behavior but mainly on understanding how the carbide volume fraction influences it.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The first is to study the influence of the microstructural features on the parameters of the Voce model [ 26 ]. The Voce model has proven its validity in modeling the plastic deformation of materials with different carbon content [ 27 ]. The second approach is a new data-driven approach based on functional data analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel

et al. 2022

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This study proposes a new approach to determine phenomenological or physical relations between microstructure features and the mechanical behavior of metals bridging advanced statistics and materials science in a study of the effect of hard precipitates on the hardening of metal alloys. Synthetic microstructures were created using multi-level Voronoi diagrams in order to control microstructure variability and then were used as samples for virtual tensile tests in a full-field crystal plasticity solver. A data-driven model based on Functional Principal Component Analysis (FPCA) was confronted with the classical Voce law for the description of uniaxial tensile curves of synthetic AISI 420 steel microstructures consisting of a ferritic matrix and increasing volume fractions of M23C6 carbides. The parameters of the two models were interpreted in terms of carbide volume fractions and texture using linear mixed-effects models.

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“…A modified version of Equation ( 6 ), usually accepted for austenitic stainless steels and in the presence of carbide precipitates [ 41 ], was proposed by Ludwigson [ 43 ]:

where

, and

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel

et al. 2022

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“…It is imperative to mention that the values of σ 0 , k L, and n L have been chosen from previously published literature. [16,27,31] (a)…”

Section: Microstructure Evolution and Selection Of Rvementioning

confidence: 99%

Microstructure‐Based Modeling and Estimation of Mechanical Properties of High‐Carbon Steel

Dey

Das

Saha

et al. 2023

steel research int.

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Herein, attempts are made to estimate the mechanical properties using microstructure‐based finite element (FE) modeling and validate these results with the experimental results. The two high‐carbon steel specimens are hot‐rolled and air‐cooled to develop ferrite–pearlite microstructures. Different characterizations are utilized to observe microstructures as well as Vickers hardness and tensile tests are carried out to determine the mechanical properties. Two high‐resolution scanning electron micrographs are chosen for representative volume element‐based FE analyses for modeling the mechanical behavior of ferrite–cementite microstructure. Object‐oriented finite elements (OOF2) and Abaqus FEA 6.14 software are used to estimate the elastic and elastoplastic behavior assuming plane stress conditions. The correlation between cementite lamellae orientation and the predicted elastoplastic properties is investigated and compared with the experimental results. The influence of image size and mesh size on the predicted true stress–true strain behavior is discussed. The hard and brittle cementite lamellae face maximum stress while the softer ferrite matrix experiences maximum strain. It is found that strain accumulation is maximum at the interfaces of ferrite and cementite. These findings are further validated by the microvoid and crack initiation spots in the fracture surface and subsurface micrographs of broken tensile specimens.

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Machine Learning‐Guided Design of Pearlitic Steel with Promising Mechanical and Tribological Properties

Qiao

Zhu

2021

Adv Eng Mater

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Herein, attempts have been made to design and develop pearlitic steels for application in heavy‐haul rails. The hardness plays a vital role in studying the mechanical and tribological properties, which is theoretically related to the alloying composition of steel. With aid of machine learning (ML) method, the particle swarm optimization (PSO) improved generalized regression neural network (GRNN) is utilized to model the relationship between composition and hardness of pearlitic steel. The results show that the designed steel exhibits superior hardness and mechanical properties with fine pearlite lamellar microstructure. In addition, the wear behavior of the steel and its wear mechanism are systematically studied by tribological testing and electron probe microanalysis (EPMA) observations of worn surface and wear particles. With composition optimization, the wear resistance has further improved as evidenced by the lower friction coefficient and reduction of wear volume. The pearlitic steels exhibit a combined wear mechanism including adhesive wear, abrasion, delamination, and plastic deformation. As a result, the designed steels offer high hardness with very good mechanical and tribological properties which are far superior to previously reported pearlitic steels. This work may assist in developing the appropriate composition to create the desired hardness, mechanical, and tribological properties.

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A “new” empirical equation to describe the strain hardening behavior of steels and other metallic materials

Cited by 18 publications

References 37 publications

A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel

A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steel

Microstructure‐Based Modeling and Estimation of Mechanical Properties of High‐Carbon Steel

Machine Learning‐Guided Design of Pearlitic Steel with Promising Mechanical and Tribological Properties

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