Micro–macro-characterisation and modelling of mechanical properties of gas metal arc welded (GMAW) DP600 steel

Ramazani, Ali; Mukherjee, Krishnendu; Abdurakhmanov, A.; Prahl, Ulrich; Schleser, Markus; Reisgen, Uwe; Bleck, Wolfgang

doi:10.1016/j.msea.2013.09.056

Cited by 60 publications

(36 citation statements)

References 34 publications

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“…Local heat input from the welding heat source induces a large temperature gradient on the workpiece and it may destroy the microstructure and hence the mechanical properties of steels [14]. This holds in particular for DP steels which contain a specific ferritic-martensitic microstructure [15,16].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Microstructure and Mechanical Properties of Resistance Spot Welded DP600 Steel

Ramazani

Mukherjee

Abdurakhmanov

et al. 2015

Metals

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Abstract:Resistance spot welding (RSW) as a predominant welding technique used for joining steels in automotive applications needs to be studied carefully in order to improve the mechanical properties of the spot welds. The objectives of the present work are to characterize the resistance spot weldment of DP600 sheet steels. The mechanical properties of the welded joints were evaluated using tensile-shear and cross-tensile tests. The time-temperature evolution during the welding cycle was measured. The microstructures observed in different sites of the welds were correlated to thermal history recorded by thermocouples in the corresponding areas. It was found that cracks initiated in the periphery region of weld nuggets with a martensitic microstructure and a pull-out failure mode was observed. It was also concluded that tempering during RSW was the main reason for hardness decrease in HAZ. OPEN ACCESSMetals 2015, 5 1705

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Section: Introductionmentioning

confidence: 99%

Characterization of Microstructure and Mechanical Properties of Resistance Spot Welded DP600 Steel

Ramazani

Mukherjee

Abdurakhmanov

et al. 2015

Metals

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“…predict the flow-stress behavior of each phase in the steel. [11][12][13][14][15]18 The former equation represents the material strengthening due to the carbon content and other alloying elements' carbide precipitation, while the latter is based on the dislocation theory determining the effects of the grain size. 12 The approach can be expressed as Equation (1):…”

Section: Experimental Partmentioning

confidence: 99%

“…For martensite, two constants, 41 and 0.038, were used to define k and L. 11,13,15,18 The second term is the solid-solution strengthening due to the carbon and nitrogen contents. …”

Section: Recentlymentioning

confidence: 99%

“…9,10 Recently, researchers obtained the flow curves of the composite phases using micromechanical modelling based on the RVEs selected from a real microstructure. [11][12][13][14][15][16][17][18][19] The flow behavior of a DP steel mainly depends on the properties of ferrite and martensite and the volume fractions of different phases. As steel has the same chemical composition, when modelling heat-treated steels, the model mainly focuses on the effects of strengthening methods and the grain size on the strength of different phases.…”

Section: Introductionmentioning

confidence: 99%

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Phase-transformation behavior and micromechanical properties of a dual-phase steel after chemical modifications

Zhao¹,

Zhao²,

Sun³

et al. 2017

Mater. Tehnol.

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In this work, the phase-transformation behavior and micromechanical properties of a dual-phase steel after chemical modifications were investigated theoretically and experimentally. In particular, the micromechanical behavior of the steel was modeled, based on the effects of the microstructure, phase fractions, local compositions of single phases and their area shapes. The developed model was used for predicting the damage behavior of a specimen. It was demonstrated that the tensile strength increased with the increasing temperature due to an increase in the amount of martensite in the steel, but the hardening behavior of this specimen was affected by the microstructure. Furthermore, the flow curves of the steel under different intercritical temperatures could be well predicted based on the real microstructures. The subsequent simulation results showed that while higher stress concentrated on the martensite, the shear-band appearance strongly depended on the microstructures of the phases. In addition, for the prediction of damage behaviors, the true stress/true strain curves of macroscale simulations showed good agreement with the experiments involving differently heat-treated steels. Keywords: intercritical treatment, steel, micro model V tem delu so teoreti~no in eksperimentalno preiskovali fazne spremembe in mikromehanske lastnosti dvofaznega jekla po kemijskih prilagoditvah. [e posebej je bilo modelirano mikromehansko obna{anje jekla glede na mikrostrukturo, dele`e posameznih faz, lokalno sestavo posameznih faz in njihovo morfologijo. Razviti model je bil uporabljen za napoved po{kodb vzorca. Dokazano je bilo, da se natezna trdnost pove~uje z nara{~ajo~o temperaturo zaradi nara{~anja vsebnosti martenzita v jeklu, vendar je na kaljivost tega vzorca vplivala mikrostruktura. Nadalje je bilo ugotovljeno, da je mo`no krivulje te~enja jekla pri razli~nih interkriti~nih temperaturah dobro napovedati na podlagi dejanskih mikrostruktur. Nadalje so rezultati simulacij pokazali, da so vi{je napetosti koncentrirane na martenzitu in isto~asna prisotnost stri`nih pasov, mo~no odvisne od mikrostruktur posameznih faz. Poleg tega je za napoved po{kodb pomembno, da se prave krivulje napetost-deformacija, dobljene s pomo~jo simulacij na makronivoju, dobro ujemajo z eksperimentalnimi, dobljenimi pri razli~no toplotno obdelanih jeklih.

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“…Arti cial Neural Networks (ANNs) are widely proposed in the literature as mathematical tools to implement the estimation methods needed in processes of this kind, because of its learning capability [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Optimization of Joint Edge Geometry and Process Parameters in Gas Metal Arc Welding Using Integrated ANN - PSO Approach

2017

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Abstract. Gas Metal Arc Welding (GMAW) is one of the most extensively used processes in automated welding due to its high productivity. However, to simultaneously achieve several con icting objectives such as reducing production time, increasing product quality, full penetration, proper joint edge geometry, and optimal selection of process parameters, a multi-criteria optimization procedure must be used. The aim of this research is to develop a multi-criteria modeling and optimization procedure for GMAW process. To simultaneously predict Weld Bead Geometry (WBG) characteristics and Heat-A ected Zone (HAZ), a Back Propagation Neural Network (BPNN) has been proposed. The experimentally derived data sets are used in training and testing of the network. Results demonstrate that the nely tuned BPNN model can closely simulate actual GMAW process with less than 1% error. Next, to simultaneously optimize process characteristics, the BPNN model is inserted into a Particle Swarm Optimization (PSO) algorithm. The proposed technique determines a set of values for parameters and the workpiece groove angle in such a way that a pre-speci ed WBG is achieved while the HAZ of the weld joint is minimized. Optimal results are veri ed through additional experiments.

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Micro–macro-characterisation and modelling of mechanical properties of gas metal arc welded (GMAW) DP600 steel

Cited by 60 publications

References 34 publications

Characterization of Microstructure and Mechanical Properties of Resistance Spot Welded DP600 Steel

Characterization of Microstructure and Mechanical Properties of Resistance Spot Welded DP600 Steel

Phase-transformation behavior and micromechanical properties of a dual-phase steel after chemical modifications

Simultaneous Optimization of Joint Edge Geometry and Process Parameters in Gas Metal Arc Welding Using Integrated ANN - PSO Approach

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