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Welding Metallurgy of

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The article was published on 1987-01-01 and is currently open access. It has received 991 citations till now. The article focuses on the topics: Welding.

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Effect of Constraint on the Fracture Behaviour of a Simulated Heat-Affected Zone of an X-70 Steel Used in Pipelines

TL;DR: In this paper, a non-equilibrium microstructure consisting of bainite, with ferrite and some martensite, has been produced and mechanically characterised, resulting in much greater hardness and strength and a lower ductility and toughness than the corresponding base metal.
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Solidification structures and phases in wire arc additive manufactured C250 maraging steel

TL;DR: In this paper , the microstructural characteristics of C250 maraging steel, in terms of solidification structures and phases, were investigated by using optical microscopy, scanning electron microscopy and transmission electron microscope (TEM).
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Investigation on Temperature Distribution in SAW Weld Using 2D Mesh Model

TL;DR: In this article, the authors studied the temperature distribution during the SAW welding with 2D heat flow mesh model and found that the shape of the weld is not formed by the time the heat source is terminated.
Journal Article

Experimental Investigation of Aluminium 6061 Alloy By Gas Tungsten Arc Welding Process

TL;DR: In this paper, the mechanical properties of the 6061 aluminium alloy single V butt joints welded using ER4043 filler metal with a gas tungsten arc welding (GTAW) were studied.
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Inverse Thermal Analysis of Ti-6Al-4V Laser Welds Using Solidification and Heat-Affected Zone Boundaries

Abstract: Temperature histories of Ti-6Al-4V laser welds are presented, which are calculated using numerical-analytical basis functions and boundary constraints based on measured solidification and heat-affected zone cross sections. These weld temperature histories can be adopted as input data to various types of computational procedures, which include numerical models for prediction of solid-state phase transformations and mechanical response. In addition, these temperature histories can be used parametrically for inverse thermal analysis of welds corresponding to other welding processes whose process conditions are within similar regimes. The present study applies an inverse thermal analysis procedure that uses three-dimensional constraint conditions whose two-dimensional projections are mapped within transverse cross sections of experimentally measured solidification and heat-affected zone boundaries.
References
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A review on selective laser sintering/melting (SLS/SLM) of aluminium alloy powders: Processing, microstructure, and properties

TL;DR: In this article, the state of the art in selective laser sintering/melting (SLS/SLM) processing of aluminium powders is reviewed from different perspectives, including powder metallurgy (P/M), pulsed electric current (PECS), and laser welding of aluminium alloys.
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Dislocation network in additive manufactured steel breaks strength–ductility trade-off

TL;DR: In this article, the authors show that the pre-existing dislocation network, which maintains its configuration during the entire plastic deformation, is an ideal modulator that is able to slow down but not entirely block the dislocation motion.
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Critical review of automotive steels spot welding: process, structure and properties

TL;DR: In this article, the fundamental understanding of structure-properties relationship in automotive steels resistance spot welds is discussed. And a brief review of friction stir spot welding, as an alternative to RSW, is also included.
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Revisiting fundamental welding concepts to improve additive manufacturing: From theory to practice

TL;DR: In this article, a unified equation to compute the energy density is proposed to compare works performed with distinct equipment and experimental conditions, covering the major process parameters: power, travel speed, heat source dimension, hatch distance, deposited layer thickness and material grain size.
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Using deep neural network with small dataset to predict material defects

TL;DR: This study attempted to predict solidification defects by DNN regression with a small dataset that contains 487 data points and found that a pre-trained and fine-tuned DNN shows better generalization performance over shallow neural network, support vector machine, and DNN trained by conventional methods.