International audiencePreviously reserved to the manufacturing of prototypes, since few years, additive manufacturing is used to manufacture metallic parts. It is the case of the Electron Beam Melting (EBM) process that is studied in this paper. The transition from prototypes to functional parts has led to increase the requirements on the produced parts. The quality of the built material has to be controlled, the geometrical tolerancings decrease and the residual stresses try to be limited. This article will focus on current main quality problem: the deformations of the part during its built due to thermal effects. The aim of this article is to show the interest to use specific melting strategies, and especially specific beam trajectories to reduce these thermal effects. First the current solution to avoid these deformations (support structures) will be discussed. In a second part the thermal phenomena causing deformations of the part will be explained in order to propose a new strategy to limit deformations in the third part. This strategy is based on the modulation of the energy input using specific beam trajectories
Wire arc additive manufacturing allows the production of metallic parts by depositing beads of weld metal using arc-welding technologies. This low-cost additive manufacturing technology has the ability to manufacture large-scale parts at a high deposition rate. However, the quality of the obtained parts is greatly affected by the various thermal phenomena present during the manufacturing process. Numerical simulation remains an effective tool for studying such phenomena. In this work, a new finite element technique is proposed in order to model metal deposition in WAAM process. This technique allows to gradually construct the mesh representing the deposited regions along the deposition path. The heat source model proposed by Goldak is adapted and combined with the proposed metal deposition technique taking into account the energy distribution between filler material and the molten pool. The effectiveness of the proposed method is validated by series of experiments, of which an example is detailed in this paper.
Managing the quality of functional parts is a key challenge in wire arc additive manufacturing. In case of additive production of aluminum parts, porosity is one of the main limitations of this process. This paper provides an indicator of porosity through the simulation of melt pool volume in aluminum wire arc additive manufacturing. First, a review of porosity formation during WAAM process is presented. This review leads to the proposal of this article: monitoring the porosity inside produced part can be achieved through the melt pool volume monitoring. An adapted Finite Element model is then proposed to determine the evolution of the melt pool volume throughout the manufacturing process of the part. This model is validated by experimental temperature measurement. Then, in order to study the link between the porosity and the melt pool volume, two test parts are chosen to access to two different pore distributions. These two parts are simulated and produced. The porosity rates of produced parts are then measured by X-ray tomography and compared to the simulated melt pool volumes. The analysis of the results highlights the interest of the melt pool volume as a predictive indicator of the porosity rate.
In order to produce functional parts in Wire Arc Additive Manufacturing (WAAM), mastering parts quality is a key challenge. The literature highlights the connection between thermal conditions and part defects. Thus, monitoring a thermal parameter, for instance the melt pool in this study, is a crucial indicator to describe parts quality. The paper aims to investigate the feasibility of CMOS camera (Complementary Metal-Oxide-Semiconductor) to track a homothety of the melt pool for parts manufactured by WAAM. In this field, the literature still lacks information concerning this sensor operating in industrial condition, especially for aluminum alloys. An experiment and a numerical method are developed to estimate its sensitivity and robustness. Validation criteria for the method are presented and confirm its interest.
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