Melt Pool Size Research Articles

Numerical investigation of the melt pool geometry evolution during selective laser melting of 316L SS

In this study, the melt pool size, precisely its width and depth, are numerically investigated for a wide range of values for both laser power and beam speed. A thermal model, developed on Ansys Additive Science, simulates the SLM of a single bead. A parametric study is achieved aiming at understanding the melt pool evolution and the defects appearing while varying these two parameters. The discussed porosity defects, namely the LOF and keyhole, are determined using the calculated melt pool dimensions and through mathematical correlations from the literature. Moreover, these numerical results are validated with experimental results for the reliability of the study. This investigation reveals a proportional relationship between the melt pool size and the laser power and an inversely proportional relationship with the scan speed. The optimal combination of these two parameters has to be well studied to avoid LOF and keyhole, which is afforded by this paper. At lower laser power levels, such as 100 W, it is advisable to choose a slower scan speed ranging from 400 to 500 mm/s. As the laser power increases, so does the optimal scan speed. For instance, with 150 W, the ideal speed falls between 600 to 900 mm/s. Similarly, for 200 W, the recommended scan speed range extends from 900 to 1200 mm/s, and for 250 W, the optimal speed range lies between 1100 and 1400 mm/s.

Training reinforcement learning-based controller using performance simulation of the laser remelting process

Surface topography modification is a common post-processing step used to enhance surface quality or to add micro/nano surface structures for various functionalities. Laser remelting (LRM) is a new technology aimed to resolve some of the shortcomings of traditional post-processing technologies. In LRM, a high-energy laser beam melts the top surface and offers control of the molten material flow through the control of laser power, scanning speed, melt pool size, and scan trajectory. By controlling the molten material flow, surface polishing by laser remelting (SP-LRM) can be used to produce finishes characterized by areal arithmetical mean height - Sa < 0.1 µm as well as surface microstructures. LRM is faster than traditional methods, creates no harmful byproducts, and allows for polishing high aspect ratio freeform surfaces without harming surrounding areas. The primary limiting factor for wide-rage industrial use of the LRM is represented by thermodynamic process instabilities that are associated with rapid melting and solidification. For instance, deep trenches or grinding marks in the initial surface can cause small deviations of local surface absorptivity to lead to laser melt pool instabilities. The high-speed nature of the LRM process combined with the complex thermodynamic phenomena makes process instabilities difficult to model, detect, predict, and control. This study aimed to use reinforcement learning (RL) in order to build a ‘self-learning’ controller capable of adapting to any material and control instabilities on-line and in real-time. Along these lines, the study will detail a preliminary application of two RL implementations involving SP-LRM performance simulation to demonstrate the implementation of a double dueling deep Q-network (DDDQN) controller for melt pool temperature distribution control. The results obtained showed that the 3-action implementation achieved 48% less variance and 35% increase in the mean value of the total reward collected compared with the 31-action implementation.

FPGA real-time melt-pool temperature control in Directed Energy Deposition

Sensing and control are key in laser-based Directed Energy Deposition (DED-LB) in terms of repeatability and good quality builds. This work presents a real-time control system where the melt-pool temperature is monitored, and the laser power is adjusted in closed-loop as control parameter. The control system includes a Field-Programmable Gate Array (FPGA) for the real-time estimation of the melt-pool temperature extracted from data from a coaxial high-speed hyperspectral camera, and a Proportional-Integral (PI) controller for the laser power regulation. The controller gains are tuned to ensure constant temperature during deposition. However, different sets of gains, with differences in the response time and transient behavior of the controller, achieve this goal but produce different deposition quality. The presented paper discusses the impact of the different controller gains on the melt-pool temperature and melt-pool size during the deposition of thin-walls and squared spirals, and compares open-loop (without control) and closed-loop (with control) cases.

Melt Pool Size Prediction of Laser Powder Bed Fusion by Process and Image Feature Fusion

Melt Pool Size Prediction of Laser Powder Bed Fusion by Process and Image Feature Fusion

Properties oriented WAAM—microstructural and geometrical control in WAAM of low-alloy steel

Today, arc-based additive manufacturing has great potential for industrial application due to new developments in robotics, welding technology, and computer-aided manufacturing. Two issues are currently the focus of research. One is the accurate generation of geometry with respect to the design, e.g., geometry fidelity, defined roughness, and shape deviations within the tolerances. Here, there are still open questions, particularly with regard to path planning and the dependence of the geometry on the selected process variables. The second topic is the adjustment or determination of the achievable mechanical and microstructural properties, as these are of crucial importance for the use of the technology in industry. The combination of both areas into a geometry- and property-oriented approach to additive manufacturing has been little discussed in the literature for arc-based welding processes. The correlations between cooling conditions and emerging properties can serve as a starting point for such a consideration. The temperature history depends on three key factors: the energy input, the interpass temperature (which in additive manufacturing is determined by the time to over-weld), and the heat transfer conditions, which are determined by the part geometry. The melt pool size or volume also depends on these three constraints. In this study, an approach is presented to realize property-oriented additive manufacturing from the interaction of property-oriented path planning and a melt pool size control system. By controlling the melt pool size, the cooling of the material can be adjusted within certain limits, and consequently, a local adjustment of the microstructure can be achieved, which greatly influences the local mechanical properties. This work demonstrates this approach for a low-alloy filler metal (DIN EN ISO 14341-A G 50 7 M21 4Mo/A5.28 ER80S-D2). Gas metal arc welding was carried out using an M21 shielding gas (82% Ar, 18% CO2). Finally, microstructural characterization will show that different microstructural morphologies and properties can be achieved in a component by combining property-oriented path planning and the use of a control loop to regulate the melt pool size.

Interplay of laser power and pore characteristics in selective laser melting of ZK60 magnesium alloys: A study based on in-situ monitoring and image analysis

This study offers significant insights into the multi-physics phenomena of the SLM process and the subsequent porosity characteristics of ZK60 Magnesium (Mg) alloys. High-speed in-situ monitoring was employed to visualise process signals in real-time, elucidating the dynamics of melt pools and vapour plumes under varying laser power conditions specifically between 40 W and 60 W. Detailed morphological analysis was performed using Scanning-Electron Microscopy (SEM), demonstrating a critical correlation between laser power and pore formation. Lower laser power led to increased pore coverage, whereas a denser structure was observed at higher laser power. This laser power influence on porosity was further confirmed via Optical Microscopy (OM) conducted on both top and cross-sectional surfaces of the samples. An increase in laser power resulted in a decrease in pore coverage and pore size, potentially leading to a denser printed part of Mg alloy. X-ray Computed Tomography (XCT) augmented these findings by providing a 3D volumetric representation of the sample internal structure, revealing an inverse relationship between laser power and overall pore volume. Lower laser power appeared to favour the formation of interconnected pores, while a reduction in interconnected pores and an increase in isolated pores were observed at higher power. The interplay between melt pool size, vapour plume effects, and laser power was found to significantly influence the resulting porosity, indicating a need for effective management of these factors to optimise the SLM process of Mg alloys.

Improving the corrosion resistance of Inconel 52M laser-cladded steel

Inconel 52M (In52M) laser-cladded steel was analyzed to study the effect of process parameters on the wire deposition mode and the melt pool geometry, which was shown to have a significant effect on the microstructural morphology and the resulting corrosion resistance of the clad. The processing parameters controlled the size and fluid flow in the molten clad pool during clad deposition. The results showed that by decreasing the size of the melt pool during cladding, the growth of coarse Al- and Ti-oxides could be substantially restricted. The presence of these coarse oxides was identified as one of the main causes of poor corrosion resistance in In52M cladded steel. The results showed that by limiting the formation and growth of these oxides, the corrosion resistance of the clads could be drastically improved. The results also showed that the corrosion resistance of In52M clads was not significantly affected by the rate of dilution of the Fe substrate into the clad, which is generally thought to be the most significant factor affecting corrosion performance. This study definitively showed that the corrosion resistance of In52M cladded steel can be considerably improved by adjusting the processing parameters to ensure a relatively small melt pool size during clad deposition, which limits the formation of harmful coarse oxides close to the outer surface of the clad, regardless of the rate of dilution.

Numerical simulation of melt pool size and flow evolution for laser powder bed fusion of powder grade Ti6Al4V

The study of molten pool characteristics is a powerful means of determining the quality of laser additive manufacturing forming. In this paper, a three-dimensional transient thermal flow field numerical model of Ti6Al4V powder processed by LPBF is developed based on FLOW-3D software, and the dynamic evolution of the molten pool with fixed process parameters is quantitatively described using dimensionless numbers in computational fluid dynamics. It is shown that the main heat transfer mode of the molten pool is thermal convection; the evaporative back sitting pressure, surface tension and Marangoni shear force are the main driving forces for the evolution of the molten pool. Furthermore, the influence of key process parameters on the heat flow field of the molten pool was analyzed. When the laser power was 300 W, the depression depth and molten pool depth caused by the recoil pressure were significantly larger, and the isothermal density of the solidification region was more intensive; when the scanning speed was increased from 0.4 m/s to 0.8 m/s, the molten pool length was reduced from 524 μm to 410 μm, and the line energy density was reduced making the amount of melted powder decrease. The wettability and fluidity of the molten state metal becomes worse; when the scanning interval increases to 120 μm, a large number of incompletely melted powder tracks cannot lap correctly at the midline of the double track. The results of the above simulations achieve a high degree of agreement with the surface quality of the specimens during the experiments, which provides guidance for quantitative analysis of molten pool evolution and prediction of Ti6Al4V forming quality.

Particle tracking in a simulated melt pool of laser powder bed fusion

Laser powder bed fusion (LPBF) is an additive manufacturing technique that prints objects layer-by-layer by selectively melting powders using a focused laser. The mechanical properties of LPBF parts are affected by processing parameters that influence the flow within the melt pool. Marangoni convection is a surface tension dependent mass transfer process from the region of lower surface tension to the region of higher surface tension, influenced by temperature and the presence of surface-active elements. The Marangoni convection-induced flow pattern in the molten metal pool can induce different surface characteristics and defects. Tracking the surface oxide particles in the melt pool can be a potential mechanism for assessing the properties of the fabricated parts. Therefore, in this work, a particle tracking algorithm was developed to track the surface oxide particles in a melt pool produced using LPBF. The flow patterns in the melt pool were observed using high-speed camera. Binary images of the melt pool were simulated using MATLAB script based on the experimental observations. The particle tracking algorithm was used for different flow patterns: radially outward, radially inward, and rotational. Various factors affecting the accuracy of the particle tracking algorithm were identified, such as melt pool size, image pixel size, size and number of surface oxides, flow pattern, and particle velocity. The image pixel size, number of surface oxides, and particle velocity were found to have maximum influence on the accuracy. The probability of error has been quantified, and the causes of errors have been explored.

On the Influence of Volumetric Energy Density and Inter-Layer Time on the Material Properties of Case-Hardening Steels

Case-hardening steels are gaining increasing interest in the field of laser powder bed fusion (PBF-LB/M) due to their excellent weldability. In combination with post-process carburization heat treatment, the surface properties can be improved to generate high-strength products. When manufacturing larger products by means of PBF-LB/M, the in situ heat accumulation and the altered cooling rates affect the resulting material properties. Therefore, the fabrication of larger products requires an understanding on the influencing factors that affect the material properties. This work investigates the effect of different volumetric energy densities (VED) on the resulting microstructural and mechanical properties. It is found that the hardness decreases continuously along the build direction. The gradient depends on the applied energy and is stronger for higher energy inputs due to heat accumulation and lowered cooling rates. Furthermore, countering strategies are investigated to avoid process-specific hardness reduction along the build direction. This includes a reduced number of parts within the build job as well as a modified inter-layer time (ILT) between consecutive layers of the specimen. Applying a moderate inter-layer time helps to counter process-specific overheating, which is indicated by an almost homogeneous material hardness and melt pool size along the build direction.

Solidification of Al12Si Melt Pool in Laser Powder Bed Fusion

An understanding of the solidification characteristics of the melt pool during laser powder bed fusion, L-PBF, metal printing is essential to ensure the manufacture of sound parts. This is the main theme of this article where it is attempted to unlock the solidification black box through calculation/measurement of the main solidification parameters of cooling rate, growth rate, temperature gradient, and undercooling. The very small melt pool size and its rapid cooling hinder the application of conventional tools to extract such information. A knowledge of these parameters and their effect on the microstructure and morphology of constituent phases could help to better control the L-PBF process and indeed all the fusion-based additive manufacturing routes, to fabricate high-quality parts. Al12Si alloy solidification during the L-PBF fabrication route is examined to highlight the extreme changes in solidification parameters and their effect on the morphology and size of eutectic Si. This is because the morphology of Si controls the mechanical properties of the finished part. The Al–Si eutectic has a divorced architecture attributed to the difficulty associated with Si growth resulted from rapid solidification. The generation of a temperature gradient of about 7700 °C/mm resulted in cooling rates in the range of 1.1 × 106 °C/s and a growth rate of 140 mm/s. Such values were then used to predict the morphology of eutectic Si based on the traditional approach.

An investigation into the effects of contouring process parameters on the up-skin surface characteristics in laser powder-bed fusion process

Laser powder-bed fusion (L-PBF) additive manufacturing is reshaping how parts are designed and built in the aerospace, automotive, biomedical, and other industries. However, in several applications, the quality of the components in terms of surface roughness, density, and mechanical properties is not comparable to that of parts made by conventional manufacturing processes. Multiple process parameters and associated uncertainties strongly influence the properties of L-PBF built parts. This study aims to understand the effects of contouring process parameters on the formation of up-skin surfaces and roughness variability in an inclined build scenario. Through Design of Experiment (DOE), the influence of the contour process parameters, such as the laser power, scanning speed, contouring offset, and incline angle (θ, between 30° and 60°), on upward-facing surfaces is investigated. Furthermore, a comprehensive multi-physics Computational Thermo-fluid model and Discrete Element Method (DEM) simulation framework is developed to shed light on how contouring scans affect the morphology of the upward-facing surface. The developed thermo-fluid model framework is used to perform multi-track multi-layer simulations of an L-PBF process. Numerical data suggests that a higher laser power enlarges the melt-pool size providing sufficient fusion at the border, hence, improving the density of up-skin regions. A higher density may primarily contribute to a better surface finish for up-skin features. The experimental result analysis shows that in the tested slope range (30° ≤ θ ≤ 60°), the up-skin surface roughness exacerbates with the increase of the incline angle and the scanning speed. On the other hand, a higher laser power will result in better surface finishes. The findings also indicate that a higher linear energy density (LED = Power/Speed) for contour scans is more favourable for the range of parameters tested in this study. Within the range of studied contouring process parameters, the maximum and minimum of average roughness (Ra) values for up-skin surfaces are 21.2 ± 2.9 µm and 8 ± 0.6 µm, respectively.

Comparative study of the microspot splitting characteristics of direct-current and pulsed cathodic vacuum arc

Ejection of macroparticles is an inherent and unavoidable characteristic of cold cathode arc discharges. The size of the cathode arc spot's melt pool is closely related to the emitted particle size, and existing studies mostly indicate that motion velocity of arc spots can be heightened primarily by magnetic fields, thereby reducing the ejection of macroparticles. However, scant consideration has been given to avoid the fundamental nature of arc spot current concentration discharge by exploring the phenomenon of arc spot splitting. In this paper, microspot splitting characteristics of direct current and pulsed cathodic vacuum arc were compared and analyzed to investigate the effect of pulsed discharges on the arc spot's internal structure, splitting, and kinematic characteristics. The results showed that pulsed arc discharges emit a dense, highly ionized plasma flow which impacts the relatively thin positive charge layer instantaneously during the peak pulsed period. This then produces a highly ionized plasma region, while also promoting an effect by which the arc spot is split into multiple microspots. Additionally, the degree of dispersion resulting from microspot splitting is positively correlated with the ratio of the plasma flow density to the concentration of the positive charge layer at the moment of microexplosion. Therefore, the microspot splitting effect of a second-order pulsed arc is weaker than that of a first-order pulsed arc. Moreover, the splitting characteristics indicated a trend toward the progressive splitting state of the direct current cathode arc. When the peak current of the single-order pulsed arc was increased from 100 to 400 A, the particle refinement effect on the surface of the CrN coating became much more evident compared to the effect of a 200 A arc in the direct current mode. And upon reaching a peak current of 500 A (and up to 600 A), the number of splits did not increase linearly due to the continuous increase in the discrete degree of microspot splitting and concurrent decrease in microspot stability. This resulted in relatively high individual microspot currents, while some relative macroparticles still appeared on the coating surface.

Melt pool size of optical glass irradiated by semiconductor laser

ABSTRACT Using a continuous laser to soften glass locally is a novel way of selective glass forming. Due to gaussian distribution of laser power density, a temperature gradient will be generated on glass treated with localized irradiation by laser, which brings about the coexistence of a glass state and a viscoelastic state. This situation creates a bulge on the surface of the softened area. Based on this phenomenon, we propose a measurement approach and a calculation model for the softening zone width of glass irradiated locally by laser and verify the calculation model through simulations and experiments. The maximum temperature deviation of the proposed temperature field prediction model is 4.7°C, while the minimum deviation is 0.53°C; making the deviation between the calculation model of the softening zone width and the experimental measurement result less than 0.319%.

Tensile behavior of AlSi10Mg overhanging structures fabricated by laser powder bed fusion: Effects of support structures and build orientation

Laser powder bed fusion (LPBF) allows the fabrication of complex parts with high resolution. In the process of manufacturing complex parts, overhanging structures with unique mechanical properties are inevitable. The mechanical properties are relevant to the microstructure that are severely influenced by the support structures and build orientation. In this study, a three-dimensional finite element model was established to numerically investigate the thermal behavior of the melt pool in the overhanging structures, and the effects of different forming conditions on tensile behavior were analyzed through the microstructure of overhanging specimens and fracture surfaces of failed specimens. The simulation results show that the change trend of the melt pool size is the same as that of the maximum temperature. Moreover, the melt pools closer to the down-facing surface of overhanging structures carry higher temperature and larger size. The application of the support structures effectively reduces the temperature of the melt pool in the starting layer by 5.3%. With the increase of inclination angle, the melt pool temperature in the starting layer decreases slightly. The experimental results indicate that the mechanical properties of 45° specimens fabricated above the entity are stronger than those of 45° specimens with support structures, and the mechanical properties of 45° specimens with support structures are stronger than those of 45° specimens without support structures. Moreover, the mechanical properties of the specimens deteriorate when the build orientation of the overhanging structures varies from 0° to 45°. The mechanical properties of different specimens with 45° build orientation are mainly affected by the thickness of defect area, and the mechanical properties of the specimens with different build orientations are mainly affected by fracture path. The results will facilitate the understanding and application of the LPBF manufacturing technique for overhanging structures in complex parts.

Prediction of melt pool width and layer height for Laser Directed Energy Deposition enabled by physics-driven temporal convolutional network

Laser Directed Energy Deposition (L-DED) is a momentous metal additive manufacturing technology. Owing to high flexibility characteristic, it has been progressively adopted by high-value manufacturing industries. For the technology, one of the fundamental research challenges is how to accurately predict the melt pool size to ensure high-quality L-DED processes. To tackle the challenge, a novel physics-driven temporal convolutional network (TCN) approach is presented. In this research, the high prediction accuracy for L-DED is achievable via the following innovations: (i) a TCN model is designed as the core of the approach to leveraging the distinctive characteristics of the TCN model to address the temporal nature of the L-DED process during heat accumulation and incremental deposition; (ii) the physical models of the peak temperature, Marangoni effect and liquid jets affecting the melt pool formulization during the L-DED process are specified to strengthen the prediction accuracy of the approach. Experiments for manufacturing thin-walled parts using L-DED were conducted for approach validation and analyses. On average, the mean absolute percentage errors (MAPEs) of predicting the melt pool width and the layer height of a melt pool attained by this approach are 3.421% and 4.643%, respectively. The experiments demonstrate that the approach is competent to support the L-DED process in producing good-quality thin-walled parts.

Data-driven prediction of geometry- and toolpath sequence-dependent intra-layer process conditions variations in laser powder bed fusion

Geometrical features and toolpath sequence are two important factors that cause process condition variations, such as variations in the meltpool temperature or meltpool size, that might lead to undesired material properties in the laser powder bed fusion (LPBF) process. Due to the high dynamics and complex physics of the LPBF process, it is difficult to predict variations in process conditions with simulations alone. Advances in measurement technology and computational technologies open up new possibilities for smart manufacturing. In this paper, a data-driven method to predict intra-layer variations in the processing conditions that source from the toolpath sequence and part geometry is presented. The approach is demonstrated using two-color on-axis pyrometer measurements. Three demonstration cases are presented in which it is demonstrated (1) how the trained predictive model can be used as a filter to ease the interpretation of process variations and discover patterns related to toolpath and part geometry, and (2) how to generate predictions that can be used for feedforward control, i.e., for adjusting laser power or scanning speed along the toolpath using a meltpool temperature prediction model generated based on on-axis measurements. Results show that the developed prediction model is able to meaningfully predict process variations resulted from toolpath sequence and geometry. Predictions are aligned with the results from the related work of others and for the case of 180° laser path turnarounds in our high-speed X-ray imaging experiments. The potential issues related to the current maturity status of the process and measuring equipment that could in practice affect the performance of the proposed solutions are also discussed.

Physics-Informed Machine Learning for Accurate Prediction of Temperature and Melt Pool Dimension in Metal Additive Manufacturing.

The temperature distribution and melt pool size have a great influence on the microstructure and mechanical behavior of metal additive manufacturing process. The numerical method can give relatively accurate results but is time-consuming and, therefore, unsuitable for in-process prediction. Owing to its remarkable capabilities, machine learning methods have been applied to predict melt pool size and temperature distribution. However, the success of traditional data-driven machine learning methods is highly dependent on the amount and quality of the training data, which is not always convenient to access. This article proposes a physics-informed machine learning (PIML) method, which integrates data and physics laws in the training parts, overcoming the problems of low speed and data availability. An artificial neural network constrained by the heat transfer equation and a small amount of labeled data is developed to predict the melt pool size and temperature distribution. Besides, the locally adaptive activation function is utilized to improve the prediction performance. The result shows that the developed PIML model can accurately predict the temperature and melt pool dimension under different scanning speeds with a small amount of labeled data, which shows significant potential in practical application.

Tracking of Marangoni driven motion during laser powder bed fusion

Laser Powder Bed Fusion (LPBF) can be used to create Functionally Graded Materials (FGMs) by selectively distributing property augmenting particles throughout the build matrix. With this method parts can be created with nonhomogeneous properties tuned to spatially varied, pre-determined design constraints. To unlock this capability, the fluid motion that occurs in the molten liquid phase must be thoroughly understood. More importantly, the actual tracking of motion during the LPBF process is needed. A numerical model of heat transfer and fluid flow in LPBF process was created. Using results from the model the motion of particles starting at many different locations was tracked. The average change in position for these particles based on starting location was calculated. The effects of non-dimensional parameters on melt pool size were examined. These results were validated experimentally and compared to what is available in the literature.

On the possibility of doing reduced order, thermo-fluid modelling of laser powder bed fusion (L-PBF) – Assessment of the importance of recoil pressure and surface tension

Meso-scale, multi-physics simulations of metal additive manufacturing (MAM) processes have so far proven their capability as a reliable tool for predicting potential defect formations. Nevertheless, there is a large number of uncertainty contributions involved with the input process parameters as well as the implemented material properties in these models. As expected, both the process-related and material-related uncertainties affect the outcome of these multi-physics simulations to a large degree. The present work is therefore intended to quantify the impacts of some of the important material/process-related uncertainties involved with meso-scale multi-physics models, on the heat transfer conditions within melt pool. In this respect, a meso-scale multi-physics model of the laser powder bed fusion process of stainless steel 316-L is developed in the commercial Finite Volume Method (FVM) based software Flow-3D and then validated against in-house experiments prior to the main investigation. In the first part of the study, the impact of recoil pressure at different laser linear energy densities (LED) and different laser beam sizes on the melt pool morphology are investigated. It is found that there is a specific threshold of LED below which the melt pool shape is not affected by the recoil pressure and the melt pool fluid dynamics is mostly governed by the Marangoni effect. This threshold increases from 80 J/mm to 280 J/mm when the beam size is increased from 20 μm to 120 μm. Moreover, a parametric study using dimensionless numbers is carried out to understand the impact of different capillary forces on the melt pool shape and size. It is observed that for inverse Bond numbers below 4.105, the depth-to-width ratio of the melt pool is above 1 where the recoil pressure dominates the melt pool dynamics and a keyhole forms. In summary, this study specifies in essence the process window over which specific physics are unimportant so that a lower-fidelity meso-scale model could replace the higher-fidelity multi-physics models.