Industrial selective laser melting (SLM) systems commonly employ a fixed set of process parameters throughout the build of the same component. The process parameters are generally found by experimental studies carried out on simple geometries which achieve high density. A common issue is related to the fact that the single set of parameters can be inadequate for small sections and overhang regions where thermal accumulation can occur. An online adaptation of process parameters is required for avoiding such issues and defects that commonly arise, such as the swelling phenomenon. A real-time control strategy would be desirable. However, the real-time control requires fast acquisition and reaction in the order of microseconds. Another approach is to provide corrective actions in a layer-wise fashion by elaborating the monitoring data collected during the previous layer. Therefore, this work proposes a layer-wise control strategy based on coaxial melt pool monitoring. For this purpose, an open SLM platform is employed, fitted with a complementary metal-oxide semiconductor camera, to view the process emission in the near infrared region. Initially, the nominal level of the melt pool area is defined on a simple geometry. Then, the melt pool area is monitored on more complex shapes. The melt pool area measured on each scan vector of a given layer is used to compensate the energy density of the same scan vector at the next layer. The results show an effective reduction of swelling defects on small geometries with fine details.
In laser powder bed fusion (LPBF), it is common practice to select process parameters to achieve high density parts starting from simple geometries such as cubes or cylinders. However, additive manufacturing (AM) is usually adopted to produce very complex geometries, where parameters should be tuned locally, depending on the local features to be processed. In fact, geometrical features, such as overhangs, acute corners, and thin walls may lead to over-or under-heating conditions, which may result in geometrical inaccuracy, high roughness, volumetric errors (i.e., porosity) or even job failure due to surface collapse. This work proposes a layer-wise control strategy to improve the geometrical precision of overhanging regions using a coaxial melt pool monitoring system. The melt-pool images acquired at each layer are used in a control-loop to adapt the process parameters locally at the next layer in order to minimize surface defects. In particular, the laser duty cycle is used as a controllable parameter to correct the energy density. This work presents the main architecture of the proposed approach, the control strategy and the experimental procedure that need to be applied to design the control parameters. The layer-wise control strategy was tested on AISI 316L stainless steel using an open LPFB platform. The results showed that the proposed layer-wise control solution results in a constant melt pool observed via the laser heated area size starting from the second layer onward, leading to a significant improvement in the geometrical accuracy of 5 mm-long bridge geometries.
Nowadays industrial laser cutting systems employ a fixed set of process parameters throughout the cut of the same workpiece, which results in a good compromise between maximum productivity and surface quality. The process parameters are commonly set by trial-and-error experiments carried out on different materials and thicknesses or less frequently by physical modelling. However, the final cut quality is not constant even though the process parameters are kept fixed due to degradation of the initial status of the laser cutting system. One of the common issues in the laser cutting process is the local heating of the optical components due to contamination and/or high powers commonly employed, which cause shifting of the focus position. This can worsen the cutting-edge quality, and even result with loss of cut. Therefore, the online measurement of the position of focus is a requirement for a consistent process. An empirical method used in the industrial practice for initially setting and successively examining and adjusting the focus position is to measure the kerf width of a straight-line cut performed with constant process parameters. This paper proposes an algorithm to monitor the kerf width and yield the estimated focus position in real-time during the cutting process. The kerf width is observed during the process with a coaxial camera module mounted on the laser head which monitors the thermal interaction between the laser beam and the material. An image processing algorithm was developed for extracting the kerf width from the acquired images, and the algorithm parameters were experimentally calibrated such that the extracted value of the kerf width matches with its physical measure. To understand the influence of the focus position on the cutting kerf, an experimental campaign was conducted and subsequently a regression model was fitted. The real-time monitoring and computation of the kerf width and its correlation to the focus position give the opportunity for a closed-loop control of the focus shift, that would eventually lead to a gain of process stability and repeatability.
Common practice in Selective Laser Melting (SLM) is employing a series of fixed process parameters throughout the whole build. However, process thermal conditions strongly depend on the local geometry of the part. Formation of some common defects, including swelling regions and elevated zones, emerges in critical corner areas due to excessive heat accumulation when constant parameters are used. Adaptation of energy input according to the geometry of the processed zone is highly desirable for avoiding defect formation. To assess the processing conditions, observation of the melt pool and its variation as a function of the process parameters with a coaxial camera operating in near infrared (NIR) demonstrated to be a feasible option. This work develops an empirical model that gives the correct amount of energy input to achieve stable melt pool depending on the single vector length, hence the part geometry. The model was validated on a prototype SLM system, and the results showed that controlling the process parameters considerably improves the geometrical accuracy of the parts with sharp edges prone to hot spot formation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.