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Structural Steels

Bio: Structural Steels is an academic researcher. The author has contributed to research in topics: Welding. The author has an hindex of 1, co-authored 1 publications receiving 792 citations.
Topics: Welding

Papers
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01 Jan 1987

991 citations


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Journal ArticleDOI
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.

1,172 citations

Journal ArticleDOI
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.

557 citations

Journal ArticleDOI
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.
Abstract: Spot welding, particularly resistance spot welding (RSW), is a critical joining process in automotive industry. The development of advanced high strength steels for applications in automotive industry is accompanied with a challenge to better understand the physical and mechanical metallurgy of these materials during RSW. The present paper critically reviews the fundamental understanding of structure–properties relationship in automotive steels resistance spot welds. The focus is on the metallurgical characteristics, hardness–microstructure correlation, interfacial to pullout failure mode transition and mechanical performance of steel resistance spot welds under quasi-static, fatigue and impact loading conditions. A brief review of friction stir spot welding, as an alternative to RSW, is also included.

369 citations

Journal ArticleDOI
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.

369 citations

Journal ArticleDOI
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.

314 citations