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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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Influence of the interpass temperature on the microstructure and mechanical properties of the weld metal (AWS A5.18 ER70S-6) of a narrow gap welded API 5L X70 pipe joint
Paulo Henrique Grossi Dornelas,João da Cruz Payão Filho,Francisco Werley Cipriano Farias,Victor Hugo Pereira Moraes e Oliveira,Diogo de Oliveira Moraes,Petrônio Zumpano Júnior +5 more
TL;DR: In this article , the effects of high interpass temperature (IT) on the weld metal (AWS A5.18 ER70S-6) of a narrow groove high-strength low-alloy steel (API 5L X70) pipe welded by a gas metal arc were investigated.
Journal ArticleDOI
Investigation of the Effects of Soaking Time on the Properties of Stainless Steel
TL;DR: In this paper, the effect of soaking time during sensitization on the mechanical properties of Austenitic stainless steels was investigated and it was concluded from the results obtained that the macro and micro hardness of AISI 316 decreases with increase in soaking time at a constant soaking temperature.
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Behaviour of coatings with AWS ERNiCrMo-3 electrode on ASTM A516 Gr.60 steel in ageing tests
Sitonio Gomes de Magalhães,Marcelo Ferreira Motta,Hélio Cordeiro de Miranda,Jesualdo Pereira Farias +3 more
TL;DR: In this article, the microstructure of nickel-based overlay welds after ageing heat treatments was evaluated using micro-hardness profiles and scanning electron microscopy together with energy dispersive spectrometry analysis.
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Review of various methods of keyhole removal in friction stir welding sheets and pipes
TL;DR: In this paper , various methods used for keyhole removal which include filling keyhole directly or indirectly is done, and general description of various methods of key hole removal for both sheets and pipes have been made.
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Surface modification technique to enhance metallurgical and mechanical properties of alloy C-276 weldment by laser shock peening without coating
S A Nithin Joseph Reddy,E Thrinadh,S. Prabhakaran,S. Kalainathan,N. Arivazhagan,M. Manikandan +5 more
TL;DR: In this paper, the effect of laser shock peening (LSP) to improve the metallurgical and mechanical properties of the weld joint was discussed. And the results showed that a fine equiaxed dendritic structure was observed in both conditions.
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.