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Opto-mechanical modelling of an additive manufacturing laser scanning head including assembly defects

Abstract : Additive manufacturing (AM) machines used in SLM or SLA are composed of galvanometric scanning system in order to focus and steer a laser beam to melt raw materials. A major problematic in these AM processes is to master the laser spot position which essentially depends on the machine geometry and the galvanometers angular positions. In literature, most of existing models make strong assumptions concerning the geometry of the laser scanning system. Moreover, the position of the laser spot is often obtained by interpolating a table of correspondence, experimentally determined, between the angular positions of the galvanometers and the cartesian coordinates in the working plane. All these approximations induce deviations of the laser spot compared to the desired position and affect machine performance. This article presents two kinematic models of the galvanometric laser scanning system in an AM machine: a nominal model of the system and a model with assembly defects consideration. These kinematic models, often used for machine tools and robots, are here applied to create a virtual AM machine. Thereby, the laser spot position can be simulated knowing the geometry of the machine, the possible assembly defects, and the orientation of the galvanometers. The second kinematic model is then used to extract the influence of assembly defects on the laser spot position. The work described in this paper allows us to highlight and quantify the theoretical impact of an assembly defect on the precision of the laser spot position in an AM machine.
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Submitted on : Wednesday, October 9, 2019 - 3:12:45 PM
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[Godineau et al 2017].pdf
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  • HAL Id : hal-02309742, version 1


Kevin Godineau, Sylvain Lavernhe, Christophe Tournier. Opto-mechanical modelling of an additive manufacturing laser scanning head including assembly defects. Joint Special Interest Group meeting between EUSPEN and ASPE : Dimensional Accuracy and Surface Finish in Additive Manufacturing, Oct 2017, Leuven, Belgium. ⟨hal-02309742⟩



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