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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
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Influence of laser welding heat input on HAZ cracking in newly developed Haynes 282 superalloy
L O Osoba,Olanrewaju A. Ojo +1 more
TL;DR: In this article, a study of the cause of heat affected zone (HAZ) cracking and its dependence on heat input during laser beam welding of a newly developed γ′ precipitation strengthened nickel based superalloy Haynes 282 was performed.
Journal ArticleDOI
Mechanical Properties and Microstructural Characterization of Cu-4.3 Pct Sn Fabricated by Selective Laser Melting
Anthony P. Ventura,C. Austin Wade,C. Austin Wade,Gregory T. Pawlikowski,Martin Bayes,Masashi Watanabe,Wojciech Z. Misiolek +6 more
TL;DR: In this article, the components were fabricated via selective laser melting (SLM) of prealloyed Cu-43 pct Sn powder and heat treated at 873 K and 1173 K (600 °C and 900 ÂC) for 1 hour.
Dissimilar metal weld joints and their performance in nuclear power plant and oil refinery conditions
Hannu Hänninen,Pertti Aaltonen,Anssi Brederholm,Ulla Ehrnstén,Hans Gripenberg,Aki Toivonen,Pitkänen Jorma,Iikka Virkkunen +7 more
TL;DR: In this article, the authors evaluated the properties of the nickel-base alloys, including the microstructure and microchemistry in the multi-pass nickel base alloy welds.
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Increase in oxidative stress levels following welding fume inhalation: a controlled human exposure study.
Halshka Graczyk,Nastassja Lewinski,Nastassja Lewinski,Jiayuan Zhao,Jiayuan Zhao,Jean-Jacques Sauvain,Guillaume Suarez,Pascal Wild,Brigitta Danuser,Michael Riediker +9 more
TL;DR: A 60-min exposure to TIG welding fume in a controlled, well-ventilated setting induced acute oxidative stress at 3 h post exposure in healthy, non-smoking apprentice welders not chronically exposed to welding fumes.
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Welding metallurgy of stainless steels during resistance spot welding Part I: fusion zone
TL;DR: In this paper, a two-part paper aims at understanding the metallurgical phenomena during resistance spot welding of stainless steels, as interesting candidates for automotive body in white, focusing on the effect of high cooling rate of the welding process on the ferrite-austenite transformation.
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.