Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection
by
Fabrizia Caiazzo 1
and Alessandra Caggiano 2,3,*
Fabrizia Caiazzo 1 and Alessandra Caggiano 2,3,*
1
Department of Industrial Engineering, University of Salerno, 84084 Fisciano (SA), Italy
2
Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
3
Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT UniNaples), 80125 Naples, Italy
*
Author to whom correspondence should be addressed.
Materials 2018, 11(4), 632; https://doi.org/10.3390/ma11040632
Received: 23 March 2018 / Revised: 13 April 2018 / Accepted: 17 April 2018 / Published: 20 April 2018
(This article belongs to the Section Manufacturing Processes and Systems)
Laser welding of titanium alloys is attracting increasing interest as an alternative to traditional joining techniques for industrial applications, with particular reference to the aerospace sector, where welded assemblies allow for the reduction of the buy-to-fly ratio, compared to other traditional mechanical joining techniques. In this research work, an investigation on laser welding of Ti–6Al–4V alloy plates is carried out through an experimental testing campaign, under different process conditions, in order to perform a characterization of the produced weld bead geometry, with the final aim of developing a cognitive methodology able to support decision-making about the selection of the suitable laser welding process parameters. The methodology is based on the employment of artificial neural networks able to identify correlations between the laser welding process parameters, with particular reference to the laser power, welding speed and defocusing distance, and the weld bead geometric features, on the basis of the collected experimental data.
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Keywords:
laser welding; titanium alloys; artificial neural networks
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MDPI and ACS Style
Caiazzo, F.; Caggiano, A. Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection. Materials 2018, 11, 632.
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