Multi-objective Optimization of Welding Parameters in Submerged Arc Welding of API X65 Steel Plates

Moradpour, M.; Hashemi, S. H.; Khalili, Khalil

doi:10.1016/s1006-706x(15)30083-2

Cited by 24 publications

(3 citation statements)

References 12 publications

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“…The welding processing parameters were used as follows: welding speed 50 cm min À1 , heat input 21 kJ cm À1 , voltage 32 V, and arc current 550 A. [28][29][30][31][32] Submerged arc welding wire H08MnMoA and flux SJ101S were selected for pipeline steel welding, as shown in Table 2. Pre-welding preheating and post-welding heat treatment were not used for welding.…”

Section: Methodsmentioning

confidence: 99%

Effect of Microstructure on Mechanical Properties of X65MS Welded Pipe Joint

Zhang,

Wei,

Wang

et al. 2024

steel research int.

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The mechanical properties of welded joints have a crucial impact on the safety of pipelines. This article focuses on examining the correlation between the microstructure and mechanical properties of welded joints of X65MS pipeline steel for sour service. The results indicate that the welding thermal cycle forms the microstructure consisting of acicular ferrite, granular bainite, and polygonal ferrite. The hardness across the joint ranges from 210 and 270 HV, peaking within the welded zone and decreasing towards the base metal (BM). At −40 °C, the welded zone shows the lowest impact energy of 53 J, while heat‐affected zones exhibit superior impact toughness exceeding 270 J with the highest impact energy of 326 J. This can be attributed to the exceptionally fine‐grained microstructure, a small quantity of small‐sized martensite‐austenite (M/A) constituents in the heat affected zone. As for the low toughness of the weld, the main factors can be attributed to the presence of large‐sized elongated M/A constituents and inclusions.

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Section: Methodsmentioning

confidence: 99%

Effect of Microstructure on Mechanical Properties of X65MS Welded Pipe Joint

Zhang,

Wei,

Wang

et al. 2024

steel research int.

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“…Das et al [17] utilised MOO with a neuro-fuzzy system to counter the inherent fuzziness present in the experimental data to develop a cluster-based input–output model of EBW process. Moradpour et al [139] proposed an MOO based on FLA and NSGA II algorithm for modelling and optimisation of the effects of different process parameters on the weld geometries during SAW of API X65 steel plates. MOO using RSM-DFA was employed by Periyasamy et al [115] to develop input–output correlations and thereby, obtain the maximum tensile strength and weld nugget of FSW joints.…”

Section: Literature Surveymentioning

confidence: 99%

Weld optimisation

Das

Jaypuria

Pratihar

et al. 2021

Science and Technology of Welding and Joining

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In welding, the concept of optimisation had been used, till date, to serve two specific purposes. To obtain any desired weld attributes, a set of required optimal input parameters could be determined utilising the principle of optimisation. To establish an input-output relationship of a welding process accurately, which might be essential to automate it, the tools for optimisation had been used extensively. These optimisation problems were formulated as either a single-objective or multi-objectives or many-objectives ones. Both traditional and natureinspired optimisation tools were used for the said purposes. This paper provides an extensive review on the role of optimisation in welding. Some research areas have also been identified for future study.

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“…Ghaderi et al [3] applied the (ICA) Imperialist competitive algorithm and (GA) Genetic algorithm. Moradpour et al [4] applied an (NSGA) non-dominated sorting genetic algorithm. Kumanan [5] created mathematical models and utilized the (PSO) particle swarm optimization approach to decrease the width of the weld.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Submerged Arc Welding Setting Parameters Using Taguchi-Based MCDM Techniques : A Comparative Analysis

Eisa

2022

ERJ. Engineering Research Journal

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The process parameters' right choice is a major problem for researchers. The sectors' decision-makers must consider a large diversity of attributes based on a group of contradicting criteria. Multi-criteria decision-making (MCDM) techniques are applied to enhance the selection of these parameters. This paper examines using the VIKOR, MOORA, and TOPSIS methodologies to determine the optimal arrangement of processing factors in submerged arc welding (SAW). Consideration is given to one case study on optimization. This case study depends on the experimental work conducted on the SAW of Cr-Mo-V steel. Significant parameters of the input process are wire feed, welding current, voltage, and speed. The influence of these factors on different responses about weld penetration, bead width, tensile strength, weld reinforcement, and weld hardness is investigated. Comparing the TOPSIS, VIKOR, and MOORA procedures demonstrate that all three techniques have shown similar results and are interchangeable. The Taguchi analysis-based TOPSIS technique is compared to the QO-Jaya algorithm, Jaya algorithm, and (TLBO) teaching learning-based optimization.

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Multi-objective Optimization of Welding Parameters in Submerged Arc Welding of API X65 Steel Plates

Cited by 24 publications

References 12 publications

Effect of Microstructure on Mechanical Properties of X65MS Welded Pipe Joint

Effect of Microstructure on Mechanical Properties of X65MS Welded Pipe Joint

Weld optimisation

Optimization of Submerged Arc Welding Setting Parameters Using Taguchi-Based MCDM Techniques : A Comparative Analysis

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