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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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Characteristics of Microstructure in Ultrahigh Carbon Steel Produced during Friction Stir Welding
TL;DR: In this paper, the feasibility of friction stir welding for ultrahigh carbon steel and the effect of welding parameters and initial microstructure on the residual micro-structure characteristics of the weld were systematically examined.
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Effect of process parameters of micro-plasma arc welding on morphology and quality in stainless steel edge joint welds
TL;DR: In this article, the effects of micro-plasma arc welding on the morphology and quality of stainless steel edge joint welds were investigated and the results indicated that the collimated shape of the low current plasma arc was mainly responsible for the low sensitivity of the weld morphology to variations in the nozzle standoff distances.
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Cracking mechanism of Hastelloy X superalloy during directed energy deposition additive manufacturing
Bojing Guo,Yashan Zhang,Zhongsheng Yang,Dingcong Cui,Feng He,Junjie Li,Zhijun Wang,Xin Lin,Jincheng Wang +8 more
TL;DR: In this paper , the cracks in DED Hastelloy X were confirmed to be solidification cracking based on extensive observations of the inner crack surface and fracture surface, and the origins of the cracking were mainly attributed to thermal stress/strain level, grain boundary (GB) characteristics (GB misorientation and GB density), and micro-alloy elements.
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Effect of active flux addition on laser welding of austenitic stainless steel
Rakesh Kaul,P. Ganesh,Nageshwar Singh,R. Jagdheesh,M. S. Bhagat,Harish Kumar,Pragya Tiwari,H.S. Vora,Ashish Kumar Nath +8 more
TL;DR: The use of active flux in tungsten inert gas (TIG) welding is known to increase the weld depth and increase the diameter of the weld as discussed by the authors, however, it is not known how to obtain a weld bead associated with humping.
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Effect of heat-input and cooling-time on bead characteristics in SAW
TL;DR: Load-carrying capacity of weld joints could be identified by its shape and size where weld bead geometry and shape relationship parameters are of utmost importance as discussed by the authors, where heat input and preheating temper...
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.