Joining
technologies

Laser beam welding

Laser beam welding technology is based on the use of a laser beam for localized melting of the materials to be joined. The laser beam is a highly concentrated heat and energy source, which gives rise to narrow and deep welds, with high welding speeds.

The main advantages of this welding technology are:

  • Low heat input welding, meaning fewer changes in microstructure and properties, in the heat affected zone.
  • Automated process, with a high degree of control.
  • High welding speeds (x5-10 compared with arc welding processes).
  • Low distortion (material deformation) and minimum residual stress caused by the process.
  • Autogenous process. Generally speaking, no filler metal is required in many applications.

  • High process reliability and repetitiveness. There is no direct contact with the parts. There is no tool wear. Long work distances are possible.
  • Applicable to a large amount of metal alloys and thermoplastic materials.
  • Adequate for welding dissimilar materials, and materials that are difficult to weld by other techniques.

All these advantages lead to high quality joints with an aesthetic appearance.


What we are currently working on

LORTEK is currently working on the following laser beam welding technology topics:

  • Laser beam welding applications for aeronautic and automotive industries, energy, railways, and capital goods with different materials. Welding speeds over 4 m/min.
  • Remote laser welding: Welding strategies based on programmed dynamic beams or wobbling.
  • Welding of battery terminals by laser (Al-Al, Cu-Cu, Al-Cu).
  • Joints without melting the substrate by means of laser brazing.
  • Welding process monitoring: seam tracking, close-loop control, quality verification after welding.
  • Precision joints between dissimilar materials that are difficult to weld.
  • Prediction of distortions in large structures, and their correction by laser straightening.

Specific Equipment

High power solid-state laser sources guided by optical fibres

  • Trumpf disc laser, up to 6 kW.
  • Trumpf 3 KW Nd-YAG laser.
  • 200-, 400- and 600-micron fibre optics.

Laser beam welding heads

  • Trumpf welding heads with and without filler metal.
  • Scansonic 3D remote welding head.
  • Scansonic welding and brazing head with filler.

Welding systems

  • KUKA-REISS robot with 7 x 3 x 1.5 m work environment.
  • 1 FANUC Arcmate 120iC robot, 6 axes + 2 additional external axes.
  • 2 Fagor CNC systems of up to 4 axes (3 linear and one rotary) with control.
  • Scansonic seam tracking sensor.
  • Thermal control sensors: pyrometers, infrared camera and vision cameras.

Publications and downloads

Publications
2021
Pedro Álvarez, Alberto Cobos, Lexuri Vázquez, Noelia Ruiz, Pedro Pablo Rodríguez, Ana Magaña, Andrea Niklas, Fernando Santos
Weldability Evaluation of Alloy 718 Investment Castings with Different Si Contents and Thermal Stories and Hot Cracking Mechanism in Their Laser Beam Welds
Metals, 2021, vol. 11, no 3, p. 402.
Open link
2019
Alvarez, P., Vázquez, L., Ruiz, N., Rodríguez, P., Magaña, A., Niklas, A., & Santos, F.
Comparison of hot cracking susceptibility of TIG and laser beam welded alloy 718 by varestraint testing.
Metals, 9(9), 985.
Open link
2019
Pedro Álvarez , Rubén Escribano, Fidel Zubiri, Fedor Fomin, Nikolai Kashaev, Stefan Bauer.
Development of laser straightening (LS) strategies to remove distortion in welded aeronautical structures.
AIP Conference Proceedings 2113, 070003 (2019).
Open link
2018
Fedor Fomin , Martin Froend, Volker Ventzke, Pedro Álvarez, Stefan Bauer, Nikolai Kashaev.
Metallurgical aspects of joining commercially pure titanium to Ti-6Al-4V alloy in a T-joint configuration by laser beam welding.
Editor: The International Journal of Advanced Manufacturing Technology, Issue 97/5-8, 2018, Page(s) 2019-2031, ISSN 0268-3768 DOI: 10.1007/s00170-018-1968-z.
Open link
2017
M. Froend, F. Fomin, S. Riekehr, S. Riekehr, P. Álvarez, F. Zubiri, S. Bauer, B. Klusemann N. Kashaev.
Fiber laser welding of dissimilar titanium (Ti-6Al-4V/cp-Ti) T-joints and their laser forming process for aircraft application.
Editor: Optics & Laser Technology, Issue 96, 2017, Page(s) 123-131, ISSN 0030-3992 DOI: 10.1016/j.optlastec.2017.05.017
Open link

Success Cases

Laser beam welding of large aeronautical structures.

Challenge

Laser beam welding of large aeronautical structures.

Solution

Laser beam welding application of Ti-6Al-4V alloys and high resistance aluminium with and without filler metal (series 2XXX and Al-Li) at high speeds (4 m/min) in structures several metres long and with curvature. A seam tracking and penetration sensor is used to ensure the joint quality.

Partners or strategic alliances

Work developed within the framework of DELASTI, LIGHTWELD and ECOTECH projects, with the collaboration of several aeronautical companies.

Laser beam welding of steel components for different applications (railway, energy, wind, household appliances, etc.)

Challenge

Laser beam welding of steel components for different applications (railway, energy, wind, household appliances, etc.).

Solution

Laser beam welding application to manufacture steel structures and components with different mechanical, aesthetic, and functional requirements.

Partners or strategic alliances

Collaboration with industrial companies from different sectors to develop and industrialize the laser beam welding process.

Remote laser welding of automotive components

Challenge

Remote laser welding of automotive components.

Solution

Remote laser welding application for joints of different materials.

Partners or strategic alliances

Collaboration with industrial companies from different sectors to develop and industrialize the laser welding process.

Challenges

Challenges to be faced in the coming years:

Welding of high reflectivity and conductivity materials (copper).
Welding of dissimilar materials with tendency to form brittle intermetallic phases.
Welding of materials of different thicknesses (tailored blanks).
Process control by monitoring and analysing parameters and physical variables of the process (temperature, plasma, acoustic emission, etc.).
Incorporation of Artificial Intelligence technologies for process optimization and self-learning.