Directed energy deposition (DED) is a widespread laser additive manufacturing process characterized by the simultaneous laser and powder delivery. During the layerwise bounding of the material, many complex interconnected physical phenomena take place in a very short time. Presently, one of the main challenges faced by DED is to enhance the powder deposition efficiency. It is defined as the ratio of powder that has been effectively solidified in the part over the total amount of powder that flowed through the nozzle while the laser was on during the deposition process. Increasing the powder efficiency would allow us to minimize the powder waste, minimize the overall costs of the DED process, and therefore reduce the printed part cost. The present work, therefore, proposes to study the influence of laser beam diameter, stand-off distance, and gas/powder settings on the DED powder efficiency. The considered gas/powder settings are carrier and shielding gas volumetric flow rate, powder mass flow rate, and particle diameter. The efficiency is computed by means of the geometry of 316L stainless steel single-track deposits. The track geometries are evaluated based on the deposition width, deposition height, and area of deposition, all extracted from the deposition profiles measured by means of laser triangulation. Optical micrographs of the single-track transversal cross sections are linked to the obtained powder efficiencies. The final aim is to get insight into the effect of process parameters on the powder efficiency and print quality and to identify the optimal process parameter combination in order to maximize the powder efficiency.

One of today’s ongoing challenges in directed energy deposition (DED) is controlling the geometry and material properties of parts. This manufacturing process is complex and nonlinear due to multiple physical phenomena at play and is therefore hard to model analytically. Machine learning (ML) on the contrary is particularly well suited to predict the behavior of a complex process with multiple inputs and outputs such as DED. A significant amount of data is required to train machine learning models, but experimental data are costly time-wise and should therefore be produced in an intelligent way. As a stepping stone for the future production of experimental training data, a finite element model of the process was developed in this study as an unlimited source of training data for the ML models. This model takes into account the printing parameters (laser speed, laser power, and powder flow rate) and outputs’ simulated process monitoring data thanks to a postprocessing method that is outlined in this article. A dataset was produced by simulating 102 tracks in 316L stainless steel with the model. From the analysis of this dataset, it was shown that K-nearest neighbors, support vector regression, decision tree regression, linear regression, and artificial neural network models are all capable of modelling the relationship between the printing parameters and the melt pool characteristics effectively.

Directed energy deposition is an additive manufacturing process that allows the production of near net shape structures. Moreover, the process can also be applied for the repair of high value components. To obtain structures with consistent good characteristics, the directed energy deposition process requires the implementation of a control system. The currently applied approaches for control that are discussed in the literature have specifically focused on melt-pool temperature control. Pyrometers have been used for such purposes; however, they provide only a single scalar value without any spatial information. In this paper, the implementation of a high-speed hyperspectral camera-based system is discussed with a high spatial resolution unlike the pyrometers. Different calibration and temperature estimation procedures for this camera-based system are evaluated and analyzed. The number of effective wavelengths needed for temperature estimation will be discussed in detail and provide an outlook on the potential of this hyperspectral camera-based system. In addition to the number of wavelengths, another important aspect of the temperature estimation methods is the stability with respect to disturbances. Within this paper, the impact of the nominal laser power will be evaluated on the stability of the temperature signals for a control system.

An effective structural health monitoring system fully exploits the flexibility offered by the 3D printing process by integrating a smart structural health monitoring technology inside the 3D-printed components. The system relies on the propagation of pressure waves with constant propagation speed through circular capillaries embedded in the structure. The nature of these waves seems to be determinant for the accuracy of the crack localization system. To achieve a better physical understanding of the nature of the propagating waves through the capillaries, computational fluid dynamics simulations are performed and compared to experimental results obtained with a self-built test setup. The presence of propagating shock waves is observed in the simulations and experiments, as well as a complex reflection mechanism around the leak location. Shock waves show the characteristic of not propagating at a constant velocity. This property complicates the actual localization system. To solve this, the constant velocity assumption should be replaced with the effective velocity evolution to increase the localization accuracy. The amplitude of the shock wave is attenuated with propagating distance, which proves that the effect of friction plays an important role and can, in turn, influence the localization system.

The experimental evaluation of melt surface flow in blown laser cladding and additive manufacturing usually employs tracer particles added to the powder feed. This paper presents details of a high-speed imaging (HSI) and image processing technique, which can directly monitor the flow of standard (nontracer) particles on the surface of the melt. This technique should improve the accuracy of flow observations as no foreign bodies are added to the melt. To verify the technique, 316 Stainless steel powder was laser clad onto a substrate of the same material, and an HSI frame rate of 40 000 frames/s was employed with a specific illumination system. The images were subsequently image processed and the trajectories of the powder feed particles on the melt surface are analyzed and discussed here. As would be expected in this case, the observed melt surface flow was in agreement with what would be expected of a melt with a negative surface tension gradient as a function of temperature.