Advances in Friction-Stir Welding and Processing (e-bog) af Asadi, P.
Asadi, P. (forfatter)

Advances in Friction-Stir Welding and Processing e-bog

1313,81 DKK (inkl. moms 1642,26 DKK)
Friction-stir welding (FSW) is a solid-state joining process primarily used on aluminum, and is also widely used for joining dissimilar metals such as aluminum, magnesium, copper and ferrous alloys. Recently, a friction-stir processing (FSP) technique based on FSW has been used for microstructural modifications, the homogenized and refined microstructure along with the reduced porosity resultin...
E-bog 1313,81 DKK
Forfattere Asadi, P. (forfatter)
Udgivet 8 december 2014
Længde 796 sider
Genrer Materials science
Sprog English
Format pdf
Beskyttelse LCP
ISBN 9780857094551
Friction-stir welding (FSW) is a solid-state joining process primarily used on aluminum, and is also widely used for joining dissimilar metals such as aluminum, magnesium, copper and ferrous alloys. Recently, a friction-stir processing (FSP) technique based on FSW has been used for microstructural modifications, the homogenized and refined microstructure along with the reduced porosity resulting in improved mechanical properties. Advances in friction-stir welding and processing deals with the processes involved in different metals and polymers, including their microstructural and mechanical properties, wear and corrosion behavior, heat flow, and simulation. The book is structured into ten chapters, covering applications of the technology; tool and welding design; material and heat flow; microstructural evolution; mechanical properties; corrosion behavior and wear properties. Later chapters cover mechanical alloying and FSP as a welding and casting repair technique; optimization and simulation of artificial neural networks; and FSW and FSP of polymers.Provides studies of the microstructural, mechanical, corrosion and wear properties of friction-stir welded and processed materialsConsiders heat generation, heat flow and material flowCovers simulation of FSW/FSP and use of artificial neural network in FSW/FSP