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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.read more
Citations
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Control of Weld Composition When Arc Welding High Strength Aluminium Alloys Using Multiple Filler Wires
TL;DR: In this paper, a method of controlling weld composition when welding Al2024 has been explored, using the tandem process and a cold wire feed unit, two and three commercially available filler wires were mixed in a single weld pool to control composition.
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Microstructure Refinement After the Addition of Titanium Particles in AZ31 Magnesium Alloy Resistance Spot Welds
TL;DR: In this article, the microstructural evolution of AZ31 magnesium alloy welds without and with the addition of titanium powders during resistance spot welding was studied using optical microscopy, scanning electron microscopy and transmission electron microscope (TEM).
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Controlling microstructure evolution and phase transformation behavior in additive manufacturing of nitinol shape memory alloys by tuning hatch distance
Jia Zhu,Evgenii Borisov,X.-H. Liang,Richard M. Huizenga,Anatoliy Popovich,Vitaliy Bliznuk,Roumen Petrov,M. J. M. Hermans,Vera Popovich +8 more
TL;DR: In this article , the effect of hatch distance on grain morphology and dislocation density evolutions in L-PBF NiTi SMAs has been investigated and a finite element model has been proposed to understand and predict the effect.
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Effect of cold metal transfer and gas tungsten arc welding processes on the metallurgical and mechanical properties of Inconel® 625 weldings
TL;DR: Inconel® 625 weld metal has lower mechanical properties than its corresponding annealed base metal due to the precipitation of brittle phases during solidification, and a reduction in heat input and amount of iron and silicon in the weld metal and an increase in the cooling rate are proposed.
Fiber laser welding of nickel-based superalloy inconel 718
TL;DR: In this article, an autogenous fiber laser welding of Inconel 718 was used to study the effects of welding parameters and different pre-weld heat treatments on liquation cracking.
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
Leifeng Liu,Qingqing Ding,Yuan Zhong,Ji Zou,Jing Wu,Yu-Lung Chiu,Jixue Li,Ze Zhang,Qian Yu,Zhijian Shen +9 more
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.