Application of Neuro-Predictive Control to Laser Beam Welding

Bollig, Alexander; Rake, H.; Kratzsch, Ch.; Kaierle, Stefan

doi:10.3182/20020721-6-es-1901.00701

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Bollig et al [8] used artificial neural network as model predictive controller, MPC, for laser beam welding process. The impact of beam power and welding speed on plasma emission, which is correlated with beam penetration depth, was analyzed.…”

Section: Weld Pool Geometry Modelmentioning

confidence: 99%

Numerical Simulation of Electron Beam Welding Process of Inconel 706 Sheets

Lacki¹,

Adamus²,

Wojsyk³

et al. 2011

KEM

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Welding operation of aircraft engine sheet part will be analyzed in this paper. The sheet part is made of narrow Inconel 706 sheet pieces. During manufacturing process first sheets undergo the process of bending. Subsequently they are welded to produce the final shape. Finite element analysis will be used to model welding operation. The thermal field and its impact on the stress field will be analyzed. The produced results will be used to design the actual welding process. Sheets will be welded using electron beam welding, EBW, method. This method is characterized by high concentration of power which instantly melts metal. As a result small HAZ is produced and comparatively small distortions are introduced. EBW process is characterized mainly by three input parameters: beam voltage, beam current and welding speed. The goal of numerical simulation is to identify the values of input parameters that produce full-depth fusion zone. As a guideline for simulation the actual dependency between input parameters and weld pool geometry will be taken from calibration data for EB welding unit. Calibration was performed using 18-8 steel. Partial least square method will be used to project those data on Inconel 706 alloy.

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Section: Weld Pool Geometry Modelmentioning

confidence: 99%