Bibliographic Details
| Title: |
STUDY OF THERMAL DISTRIBUTION AND CLADDING GEOMETRY DURING LASER METAL DEPOSITION PROCESS USING FINITE ELEMENT ANALYSIS. |
| Authors: |
Rusu, Andrei1,2 andrei.rusu@inflpr.ro, Bunea, Alexandru1,2 alexandru.bunea@inflpr.ro, Ghiculescu, Daniel1 daniel.ghiculescu@upb.ro |
| Source: |
Nonconventional Technologies Review / Revista de Tehnologii Neconventionale. Mar2025, Vol. 29 Issue 1, p73-79. 7p. |
| Subjects: |
Thermophysical properties, Solid geometry, Finite element method, Heat transfer, Gaussian distribution, Laser deposition |
| Abstract: |
The paper deals with additive manufacturing process for coating and 3D printing metallic materials known in literature as laser cladding, laser melting deposition, laser engineering net shaping or direct energy deposition. In this process a melt pool was generated at the interaction of the laser beam with the blown powder guided through a copper nozzle. A 3D finite element model was established to simulate laser cladding process taking into account heat transfer in solids and geometry deformation modules from COMSOL Multiphysics software. A time-dependent study was conducted to manage the computational time of the numerical model. Boundary conditions were established by introducing a Gaussian distribution for the laser energy, as well as for the velocity and shape of the resulting track. Additionally, the thermal properties of the material under investigation were taken into account. Thermal analysis was conducted to determine the temperature history during the process using a heat transfer module, while the dynamic shape of the molten zone was represented by a moving mesh based on a deformed geometry module. [ABSTRACT FROM AUTHOR] |
|
Copyright of Nonconventional Technologies Review / Revista de Tehnologii Neconventionale is the property of Asociatia Romana de Tehnologii Neconventionale (ARTN) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |