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2 results on '"Powder filler metal"'

1. Process Parameters Optimization of Laser Beam Welded Joints by Neural Network.

Author

Missori, S., Sili, A., and Ucciardello, N.

Subjects
SCANNING electron microscopy, IRON & steel plates, WELDING, LASER beams, WELDABILITY of metals, METALLOGRAPHY
Abstract

Laser beam welding of C-Mn steel plates with Ni powder filler metal has been performed. Metallography samples of the welded cross-section have been observed by scanning electron microscopy (SEM) and submitted to energy dispersive spectroscopy to obtain Ni concentration profiles. On the basis of the experimental results, neural networks have been carried out. These networks were first validated and then utilized to foresee Ni concentration along the welded thickness. The objective of obtaining the best Ni penetration and minimizing powder loss was reached optimizing, by numerical simulation, process parameters, such as powder rate and joint geometry. [ABSTRACT FROM AUTHOR]

Published
2008
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2. Process parameters optimization of laser beam welded joints by neural network

Author

A. Sili, S. Missori, and Nadia Ucciardello

Subjects
Materials science, Bevelled edge, Butt welded joint, Laser beam, Levenburg–Marquardt algorithm, Metallography, Microanalysis, Multilayer feedforward network, Neural network, Powder filler metal, Powder loss, Powder rate, Square edge, Steel plates, Welding, Welding speed, Scanning electron microscope, neural network, multilayer feed forward network, Energy-dispersive X-ray spectroscopy, powder rate, Settore ING-IND/21 - Metallurgia, Industrial and Manufacturing Engineering, butt welded joint, law.invention, microanalysis, law, General Materials Science, bevelled edge, laser beam, Levenburg Marquardt algorithm, metallography, powder filler metal, powder loss, square edge, steel plates, welding, welding speed, Filler metal, Computer simulation, Mechanical Engineering, Metallurgy, Laser beam welding, Bevel, Mechanics of Materials
Abstract

Laser beam welding of C–Mn steel plates with Ni powder filler metal has been performed. Metallography samples of the welded cross-section have been observed by scanning electron microscopy (SEM) and submitted to energy dispersive spectroscopy to obtain Ni concentration profiles. On the basis of the experimental results, neural networks have been carried out. These networks were first validated and then utilized to foresee Ni concentration along the welded thickness. The objective of obtaining the best Ni penetration and minimizing powder loss was reached optimizing, by numerical simulation, process parameters, such as powder rate and joint geometry.

Published
2008

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