Next Article in Journal
Experimental Investigation on the Residual Stresses in a Thick Joint with a Partial Repair Weld Using Multiple-Cut Contour Method
Previous Article in Journal
Morphological Evolution of Vertically Standing Molybdenum Disulfide Nanosheets by Chemical Vapor Deposition
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Materials 2018, 11(4), 632; https://doi.org/10.3390/ma11040632

Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection

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.
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)
Full-Text   |   PDF [4326 KB, uploaded 3 May 2018]   |  

Abstract

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. View Full-Text
Keywords: laser welding; titanium alloys; artificial neural networks laser welding; titanium alloys; artificial neural networks
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Caiazzo, F.; Caggiano, A. Investigation of Laser Welding of Ti Alloys for Cognitive Process Parameters Selection. Materials 2018, 11, 632.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Materials EISSN 1996-1944 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top