Open Access
Welding Metallurgy of
Reads0
Chats0
About:
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
More filters
Journal ArticleDOI
Viewpoint: metallurgical aspects of powder bed metal additive manufacturing
TL;DR: The physical mechanisms of metal additive manufacturing are grounded in metallurgy, branching into laser physics and the physics of granular materials as mentioned in this paper, and the physical mechanisms control the effects of processing parameters on microstructures and properties of additively manufactured parts.
Journal ArticleDOI
Co-precipitation of nanoscale particles in steels with ultra-high strength for a new era
TL;DR: In this paper, the authors highlight recent advances in computation-aided alloy design, nanostructural characterization, and unique properties of newly developed nanoscale co-precipitation-strengthened steels.
Journal ArticleDOI
Microstructural characterization and properties of selective laser melted maraging steel with different build directions
TL;DR: In this paper, a nearly fully dense grade 300 maraging steel was fabricated by selective laser melting (SLM) additive manufacturing with optimum laser parameters, and different heat treatments were elaborately applie...
Journal ArticleDOI
In situ measurement and modelling of austenite grain growth in a Ti/Nb microalloyed steel
TL;DR: In this article, a grain growth model was developed, which includes the pinning effect of precipitates present in the steel, and an approach was developed to estimate the initial distribution of precipitate in the as-received material and their dissolution kinetics.
Journal ArticleDOI
Marangoni convection and weld shape variations in Ar-O2 and Ar-CO2 shielded GTA welding
TL;DR: In this article, the role of the oxide layer on the Marangoni convection on the pool surface at elevated temperature has been investigated and it was shown that the heavy oxide layer inhibited the fluid flow induced by the MARANGONA convection and also became a barrier for the oxygen absorption into the molten weld pool.
References
More filters
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.