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Research Papers

The Use of Genetic Algorithms to Calibrate Johnson–Cook Strength and Failure Parameters of AISI/SAE 1018 Steel

[+] Author and Article Information
M. F. Buchely

Department of Materials
Science and Engineering,
Missouri University of
Science and Technology,
1400 N. Bishop, McNutt Hall,
Rolla, MO 65409
e-mail: buchelym@mst.edu

X. Wang

Department of Mechanical and
Aerospace Engineering,
Missouri University of
Science and Technology,
400 West 13th Street,
Toomey Hall,
Rolla, MO 65409

D. C. Van Aken, R. J. O'Malley, S. Lekakh

Department of Materials
Science and Engineering,
Missouri University of
Science and Technology,
1400 N. Bishop, McNutt Hall,
Rolla, MO 65409

K. Chandrashekhara

Department of Mechanical and
Aerospace Engineering,
Missouri University of
Science and Technology,
400 West 13th Street, Toomey Hall,
Rolla, MO 65409

1Corresponding author.

Contributed by the Materials Division of ASME for publication in the JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY. Manuscript received September 20, 2018; final manuscript received November 16, 2018; published online February 13, 2019. Assoc. Editor: Peter W. Chung.

J. Eng. Mater. Technol 141(2), 021012 (Feb 13, 2019) (12 pages) Paper No: MATS-18-1265; doi: 10.1115/1.4042382 History: Received September 20, 2018; Revised November 16, 2018

Johnson–Cook (JC) strength and failure models have been widely used in finite element analysis (FEA) to solve a variety of thermo-mechanical problems. There are many techniques to determine the required JC parameters; however, a best practice to obtain the most reliable JC parameters has not yet been proposed. In this paper, a genetic-algorithm-based optimization strategy is proposed to calibrate the JC strength and failure model parameters of AISI/SAE 1018 steel. Experimental data were obtained from tensile tests performed for different specimen geometries at varying strain rates and temperatures. FEA was performed for each tensile test. A genetic algorithm was used to determine the optimum JC parameters that best fit the experimental force-displacement data. Calibrated JC parameters were implemented in FEA to simulate the impact tests of standard V-notch Charpy bars to verify the damage mechanism in the material. Considering good agreement of the experimental and FEA results, the current strategy is suggested for calibration proposes in other kind of materials in which plastic behavior could be represented by the JC strength and failure models.

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Figures

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Fig. 1

Schematic true stress—plastic strain plot showing an element undergoing stiffness reduction due to damage evolution. The dashed line represents the predicted flow stress without damage, and the solid line is the flow stress due to damage evolution.

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Fig. 3

Round specimens in accordance with the ASTM E8-16 (subsize 4) standard. Different notch geometries for testing at various levels of triaxiality: (a) No notch (low triaxiality ≈0.33), (b) Notch 1 (high triaxiality ≈1.18), and (c) Notch 2 (medium triaxiality ≈0.7). All dimensions are in inches.

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Fig. 2

Microstructure of AISI/SAE 1018 grade steel showing the short-longitudinal plane. The rolling direction is aligned with the horizontal axis. Specimen was etched with 2% nital.

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Fig. 4

FE model: (a) half of the tensile specimen (hatched area) was modeled without including grips to reduce calculation time, (b) 2D axis-symmetric model of the simplified tensile test, showing boundary and initial conditions, and (c) detail of the central area of the model. A taper shape was modeled to favor neck formation. Also, a finer mesh was used at the minimum diameter to model necking.

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Fig. 5

FE model of ASTM E23-16b Charpy impact test showing specimen and tupp

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Fig. 6

Engineering stress–strain curves of the 1018 steel obtained by tensile tests at (a) different strain rates and 25 °C and (b) different temperatures and 1 s−1

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Fig. 12

Progress of the damage evolution criteria (D or SDEG in abaqus) in the FE mode of the tensile test for 1018 steel at 1 s−1 and 25 °C. JC strength and failure models were used in the simulation. The 2D axis-symmetric plane is shown.

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Fig. 13

Comparison of force–displacement curves predicted by FEA using GA (circle markers) and Traditional (triangle markers) calibration methodologies for the failure model parameters. Experimental data (solid line) and FE results without failure (dash line) are also shown.

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Fig. 9

Comparison of measured stress and predicted stress using JC equation and calibrated JC strength parameters determined by (a) GA optimization and (b) traditional methodology

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Fig. 10

FE results of the tensile test for 1018 steel at 1 s−1 and 25 °C ambient temperature using the JC strength model only: (a) Mises stress (MPa), (b) equivalent strain rates (s−1), (c) equivalent plastic strain, (d) triaxiality (MPa), and (e) temperature (K). The 2D axis-symmetric plane is only shown.

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Fig. 11

Comparison of experimental force–displacement curves (solid line) with FE results simulated without failure (dash line) and with failure model (circle markers): (a) 1 s−1 and 25 °C, (b) 15 s−1 and 25 °C, (c) 1 s−1 and 100 °C, and (d) 1 s−1 and 25 °C for Notch 2 geometry

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Fig. 7

True stress–true strain curves of the 1018 steel at different test conditions

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Fig. 8

Profile of failed tensile specimens at different test conditions: (a) 1 s−1 and 25 °C, (b) 15 s−1 and 15 °C, (c) 1 s−1 and 250 °C, and (d) 1 s−1 and 25 °C for specimen with Notch 1. Scale in 1/128 fraction of inch.

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Fig. 17

Specimen after impact test. Scale in 1/128 fraction of inch.

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Fig. 18

Adiabatic stress–strain curves for ARMCO IROM at different conditions using JC strength model and parameters from Table 7

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Fig. 19

Predicted force–displacement for ARMCO IRON at different conditions: curves with and without failure model, using parameters from Table 7

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Fig. 14

Progress of the damage initiation criterion (DIni or JCCRT in abaqus) in the FE mode of the tensile test for 1018 steel at 1 s−1 and 25 °C. JC strength and failure models were used in the simulation. The 2D axis-symmetric plane is shown.

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Fig. 15

FE results of the Charpy test, showing the change of DIni in the specimen

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Fig. 16

FE results of the Charpy test, showing the change of damage evolution D in the specimen

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