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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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Friction stir welding of AISI 1080 steel using liquid CO2 for enhanced toughness and ductility
TL;DR: In this article, the joint structures and mechanical properties obtained under both sets of conditions were investigated, whereupon it was found that defect free joints can be successfully fabricated under either conditions, where the resulting joint microstructures mainly consisted of grain refined ferrite with some globular cementite.
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Welding metallurgy of stainless steels during resistance spot welding Part II –heat affected zone and mechanical performance
TL;DR: In this paper, the physical and mechanical metallurgy of stainless steels, as interesting candidates for automotive application, during resistance spot welding is discussed, and failure modes and mechanical properties of stainless steel resistance spot welds are compared with advanced high strength steels.
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Cold cracking studies on low alloy steel weldments: effect of filler metal composition
TL;DR: In this article, the authors used Austenitic stainless steel filler (AWS E312), which is reported to be resistant to hydrogen assisted cracking, was used to study the cracking tendency of all the three steels.
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Influences of depositing materials, processing parameters and heating conditions on material characteristics of laser-cladded hypereutectoid rails
Quan Lai,Ralph Abrahams,Wenyi Yan,Cong Qiu,Peter Mutton,Anna Paradowska,Mehdi Soodi,Xinhua Wu +7 more
TL;DR: In this article, the effects of different cladding materials, processing parameters and heating regimes on the underlying microstructural features and mechanical properties of laser-cladded premium rails were investigated by using a hypereutectoid rail grade as a substrate, which is extensively used in heavy-haul rail systems.
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Process-Dependent Composition, Microstructure, and Printability of Al-Zn-Mg and Al-Zn-Mg-Sc-Zr Alloys Manufactured by Laser Powder Bed Fusion
TL;DR: In this paper, the microstructure of Al-Zn-Mg-Sc-Zr alloys was investigated and the Scheil-Gulliver non-equilibrium solidification calculations showed that the AlZnMg alloys with lower Zn and Mg concentrations had smaller solidification range and steepness values (i.e., ΔT) and corresponded to a lower cracking severity.
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.