Prediction of HAZ width and toughness of HY85 steel using artificial neural network

Singh, Pravesh Kumar; Rajput, S.K.; Kumar, Sanjeev

doi:10.1080/2374068x.2020.1860498

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Okoye et al reported that SF-incorporated GPC has significantly higher resistance in 2% H 2 SO 4 and 5% NaCl solution, as compared to the standard concrete [13]. A number of researchers have validated the test results by establishing correlations using artificial neural network techniques [14][15][16][17]. Shahmansouri et al found that the SF incorporation up to 30% in GGBS based geopolymer concrete can enhance the strength in compression, tension, and flexure by 30%, 25%, and 20%, respectively [18].…”

Section: Introductionmentioning

confidence: 99%

Mechanical and Durability Characteristics Assessment of Geopolymer Composite (GPC) at Varying Silica Fume Content

Gupta

Saxena

2021

J. Compos. Sci.

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The present study aimed at assessing mechanical and durability characteristics of ground granulated blast furnace slag (GGBS)-based geopolymer composites at 5%, 10%, 15%, 20%, 25%, and 30% replacement proportion of silica fume at 12 molarity of NaOH. Mechanical properties were assessed using compression and tension tests, whereas durability characteristics were evaluated using ultrasonic pulse velocity test (UPV), acid test, and rapid chloride permeability test (RCPT), and water absorption (WA) test. Additionally, reduction in mass and strength were also determined due to the acid action on the developed composites. A correlation of compressive strength was also established with the splitting tensile strength, UPV, RCPT, and WA. The presence of silica fume and high NaOH concentration in GPC tends to improve the mechanical strength up toa certain level. UPV values obtained were falling in the range of medium to good category. Chloride ion penetration and water absorption values were reduced by around 23% and 26%, respectively, at 10% silica fume replacement. Mass loss and strength loss were reduced as the % of silica fume increased. A good correlation of compressive strength was obtained with tensile strength, UPV, and RCPT with a coefficient of determination of 0.9681, 0.9665, and 0.9208, respectively. Poor correlation was obtained between compressive strength and water absorption.

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Section: Introductionmentioning

confidence: 99%

Mechanical and Durability Characteristics Assessment of Geopolymer Composite (GPC) at Varying Silica Fume Content

Gupta

Saxena

2021

J. Compos. Sci.

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“…Vieira and Lambros [ 26 ] developed predictive models to estimate relations between a material’s granular microstructure and the accumulation of plastic strains at the microstructural level during plastic deformation. Physical weld simulations of single pass welding of HY 85 steel using the Gleeble ® 3800 thermo-mechanical simulator was performed by [ 27 ] to develop an ANN-based model for the estimation of the width and impact toughness of coarse grain heat-affected (CGHAZ) zone of simulated HAZ samples. The authors found good correlations with the backpropagation algorithm used with calculated relative error of ±3.15% for width and ±7.93% for impact toughness.…”

Section: Introductionmentioning

confidence: 99%

Mild Steel GMA Welds Microstructural Analysis and Estimation Using Sensor Fusion and Neural Network Modeling

Caio

Silva

Bestard

et al. 2021

Sensors

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This study aims at evaluating the efficiency of sensor fusion, based on neural networks, to estimate the microstructural characteristics of both the weld bead and base material in GMAW processes. The weld beads of AWS ER70S-6 wire were deposited on SAE 1020 steel plates varying welding voltage, welding speed, and wire-feed speed. The thermal behavior of the material during the process execution was analyzed using thermographic information gathered by an infrared camera. The microstructure was characterized by optical (confocal) microscopy, scanning electron microscopy, and X-ray Diffraction tests. Finally, models for estimating the weld bead microstructure were developed by fusing all the information through a neural network modeling approach. A R value of 0.99472 was observed for modelling all zones of microstructure in the same ANN using Bayesian Regularization with 17 and 15 neurons in the first and second hidden layers, respectively, with 4 training runs (which was the lowest R value among all tested configurations). The results obtained prove that RNAs can be used to assist the project of welded joints as they make it possible to estimate the extension of HAZ.

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Experimental study of the mechanical and durability properties of Slag and Calcined Clay based geopolymer composite

Gupta

Saxena

2021

Advances in Materials and Processing Technologies

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Prediction of HAZ width and toughness of HY85 steel using artificial neural network

Cited by 4 publications

References 25 publications

Mechanical and Durability Characteristics Assessment of Geopolymer Composite (GPC) at Varying Silica Fume Content

Mechanical and Durability Characteristics Assessment of Geopolymer Composite (GPC) at Varying Silica Fume Content

Mild Steel GMA Welds Microstructural Analysis and Estimation Using Sensor Fusion and Neural Network Modeling

Experimental study of the mechanical and durability properties of Slag and Calcined Clay based geopolymer composite

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