Melt Pool Dimensions Research Articles

Comparative Analysis of Melt Pool Evolution in Selective Laser Melting of Inconel 625 and Inconel 718 Nickel-Based Superalloys

One of the key advantages of Additive Manufacturing is the versatility in working with a wide range of materials. Among these materials, Nickel-based superalloys have drawn great attention of specialists. This study investigates the behavior of Inconel 625 and Inconel 718 during selective laser melting. While these alloys have many similarities, thus their distinct chemical compositions determine different responses to this new process, which the authors aimed to elucidate in this study. Numerical simulations using ANSYS Additive® software were conducted to compare the melt pool dimensions (depth and width) of Inconel 625 and Inconel 718. The results reveal that the material's thermal properties play a significant role in determining the melt pool geometry. The Inconel 718 consistently exhibited larger melt pool dimensions than Inconel 625. The findings highlight the importance of understanding the connection between the material properties and process parameters.

A Supervised Machine Learning Model for Regression to Predict Melt Pool Formation and Morphology in Laser Powder Bed Fusion

In the additive manufacturing laser powder bed fusion (L-PBF) process, the optimization of the print process parameters and the development of conduction zones in the laser power (P) and scanning speed (V) parameter spaces are critical to meeting production quality, productivity, and volume goals. In this paper, we propose the use of a machine learning approach during the process parameter development to predict the melt pool dimensions as a function of the P/V combination. This approach turns out to be useful in speeding up the identification of the printability map of the material and defining the conduction zone during the development phase. Moreover, a machine learning method allows for an accurate investigation of the most promising configurations in the P-V space, facilitating the optimization and identification of the P-V set with the highest productivity. This approach is validated by an experimental campaign carried out on samples of Inconel 718, and the effects of some additional parameters, such as the layer thickness (in the range of 30 to 90 microns) and the preheating temperature of the building platform, are evaluated. More specifically, the experimental data have been used to train supervised machine learning models for regression using the KNIME Analytics Platform (version 4.7.7). An AutoML (node for regression) tool is used to identify the most appropriate model based on the evaluation of R2 and MAE scores. The gradient boosted tree model also performs best compared to Rosenthal’s analytical model.

Prediction of Melt Pool Dimension, Porosity Generation and Thermal Behavior of Ti-6Al-4V during Powder Bed Fusion at Various Scan Speeds and Laser Powers

The additive manufacturing technology called laser powder bed fusion enables to manufacture complex parts based on the fusion of a metallic powder layer by layer. In laser powder bed fusion, the produced component quality relies significantly on the parameters of the process. In this study, the powder titanium alloy Ti-6Al-4V is employed for the purpose of predicting the melt pool dimensions. To manufacture a single bead, several combinations of scan speed and laser power are used. This research studies the influence of the scan speed and the laser power on the melt pool dimensions and on the thermal history of a specified layer of powder. The results reveal that the geometry of the melt pool is considerably responsive to the scan speed and the laser power. Furthermore, unfavorable effects such as porosity defects are analyzed in detail. Suggestions are presented to employ optimal settings to prevent these undesirable outcomes. To validate the numerical results, a comparison with experimental results from the literature is carried out. Our numerical analysis proves a satisfactory correlation with the experimental investigations. The beam power and the scanning speed effects on the average temperature of the desired layers are discussed as well.

Design of multi-arc collaborative additive manufacturing system and forming performance research

In view of the key issue of high-quality wire-arc-additive-manufacturing (WAAM) of large metal components, this paper has devised a suite of multi-arc collaborative additive manufacturing equipment endowed with functionalities for controlling arc behavior, managing melt pool dimensions, conducting 3D measurements, and executing subtractive machining. In the initial phase, a cutting-edge five-arc additive manufacturing head apparatus was meticulously engineered. This apparatus ingeniously blends two single-wire torches augmented by three-wire oscillating torches, synergizing deposition metal performance and forming effective. Subsequently, extensive research was carried out to explore a master-slave control-based method for orchestrating the synchronized movements of multiple robotic entities. This diligent investigation culminated in the seamless integration of a multi-arc collaborative additive manufacturing equipment, comprising five-arc high-efficiency additive units, a 3D measurement module, and a precision-driven subtractive machining unit. The influence of different horizontal distances between arc guns on the temperature field and mechanical properties of components was investigated through process experiments. The research results indicate that as the horizontal spacing between the arc guns increases, the cooling rate of the deposited metal gradually slows down, leading to an extended period of elevated temperatures. As a result, both tensile strength and yield strength gradually decrease, while elongation and impact absorption capacity first increase and then decrease. In the end, the developed equipment was utilized to fabricate large ship propeller bracket components, achieving a remarkable 4.7 times increase in manufacturing efficiency compared to single-arc additive manufacturing.

Optimization of Butt-joint laser welding parameters for elimination of angular distortion using High-fidelity simulations and Machine learning

The high temperature gradient and high cooling rate induced in the butt-joint laser welding process generate a high residual stress, which can result in significant distortion of the final weld. Previous researches usually employed experimental approach to search for optimal parameters which is costly and time consuming. Additionally, there was several studies that used simulation to compute the distortion and to find optimal parameters, however, these studies neglect the effect of solid cooling rate and size of the heat-affected zone (HAZ) in the process of finding optimal parameters. Accordingly, the present study proposes a new systematic framework based on finite element (FE) simulations, regression analysis, and two artificial neural networks (ANNs) for determining the laser welding parameters (i.e., the laser power and welding speed) which minimize the angular distortion of the laser-welded butt joint and enhance the mechanical properties. In the proposed approach, to determine the variables of the selected double-conical heat source model for any combination of the laser power and welding speed is studied within the feasible design space. The variables are then imported into a three-dimensional COMSOL Multiphysics FE model to predict the melt pool dimensions, solid cooling rate, and size of the HAZ as a function of the laser power and welding speed. Finally, the processing map produced by the ANN is screened by four quality criteria based on the melt pool depth, angular distortion, cooling rate, and HAZ width, respectively, to determine the optimal laser welding parameters which minimize the angular distortion and maximize the mechanical properties. It shows that SS316L tensile test specimens fabricated using the optimal parameters have a tensile strength (662 MPa) which is higher than that of the base metal (660 MPa). Furthermore, the angular distortion of the butt-joint samples is less than 564 µm. Thus, the effectiveness of the proposed optimization procedure is confirmed.

Unravelling keyhole instabilities and laser absorption dynamics during laser irradiation of Ti6Al4V: A high-fidelity thermo-fluidic study

A 3D multi-phase thermo-fluidic solver is implemented to investigate the laser-melt pool interaction dynamics and keyhole instabilities during stationary and moving laser irradiation. The physical phenomena such as phase changes, thermocapillary driven flow, multiple reflections of the Gaussian laser beam and beam trapping, vaporization recoil pressure, and heat losses (convective, radiative, and evaporative) have been incorporated in the model. A detailed parametric study has been carried out to investigate the influence of the laser beam spot diameter and the traversal speed on the laser absorption dynamics and melt pool characteristics. For both the stationary and moving laser irradiation, it is found that during the initial transient stage, the melt pool morphology belongs to the conduction melting mode with laser absorption according to the normal incidence condition of the Fresnel equation. Later, depending on the irradiating laser intensity, a melt transition occurs from conduction to the keyhole mode of melting, during which laser absorption rises sharply. The laser absorption and the vapor depression morphology in the keyhole regime show substantial oscillations (periodic and non-periodic) due to the complex hydrodynamics of the recoil pressure, the surface-tension dynamics, and convection-driven flow resulting in the opening and closing of the vapor depression. These oscillations are quantified, and a direct correlation of the laser absorptance with the keyhole depression depth has been observed for stationary laser irradiation. The keyhole closure mechanism and bubble trapping-ejection phenomena have been explored during the solidification stage. The numerically predicted melt pool dimensions match well with the in-house performed experimental measurements and published benchmarking experiments combining integrating sphere radiometry and in-situ X-ray radiography.

Track-scale Anisotropic Thermal Material Model as a Viable Substitution In Selective Laser Melting

High temperature gradients and resulting residual stresses are the main sources of defects such as cracks, distortion and fatigue failure is selective laser melting. In this paper, a new material model utilizing anisotropic thermal conduction model and track-scale heat input model is used to predict the melt pool geometry, material state and thermal history during the SLM process of SS316L in a large range of laser parameters. The model takes into consideration both the material state and the issue of phase transition during the track-scale simulation.. The simulated melt pools in the beam-scale and track-scale simulations are compared with experimental measurements in different laser parameters. It is found that the proposed material model is able to maintain accuracy between the beam-scale and track-scale simulations at an average of 5μm regarding melt pool dimensions. Furthermore, It can be inferred that both track-scale and beam-scale models exhibit the capability to provide precise predictions of melt pool geometries when compared to experimental measurements while the former being about 100 times faster than the latter. An average error of 10% was concluded for the material state comparison of the two models while the track-scale model was able to capture the temperature profile and cooling rate accurately in comparison with the beam-scale model.

In situ observation of melt pool evolution in ultrasonic vibration-assisted directed energy deposition

The presence of defects, such as pores, in materials processed using additive manufacturing represents a challenge during the manufacturing of many engineering components. Recently, ultrasonic vibration-assisted (UV-A) directed energy deposition (DED) has been shown to reduce porosity, promote grain refinement, and enhance mechanical performance in metal components. Whereas it is evident that the formation of such microstructural features is affected by the melt pool behavior, the specific mechanisms by which ultrasonic vibration (UV) influences the melt pool remain elusive. In the present investigation, UV was applied in situ to DED of 316L stainless steel single tracks and bulk parts. For the first time, high-speed video imaging and thermal imaging were implemented in situ to quantitatively correlate the application of UV to melt pool evolution in DED. Extensive imaging data were coupled with in-depth microstructural characterization to develop a statistically robust dataset describing the observed phenomena. Our findings show that UV increases the melt pool peak temperature and dimensions, while improving the wettability of injected particles with the melt pool surface and reducing particle residence time. Near the substrate, we observe that UV results in a 92% decrease in porosity, and a 54% decrease in dendritic arm spacing. The effect of UV on the melt pool is caused by the combined mechanisms of acoustic cavitation, ultrasound absorption, and acoustic streaming. Through in situ imaging we demonstrate quantitatively that these phenomena, acting simultaneously, effectively diminish with increasing build height and size due to acoustic attenuation, consequently decreasing the positive effect of implementing UV-A DED. Thus, this research provides valuable insight into the value of in situ imaging, as well as the effects of UV on DED melt pool dynamics, the stochastic interactions between the melt pool and incoming powder particles, and the limitations of build geometry on the UV-A DED technique.

Prediction of melt pool geometry by fusing experimental and simulation data

Accurate prediction of the melt pool geometry in laser-directed energy deposition (L-DED) is crucial for ensuring product quality. Although machine learning methods have offered aid in addressing this challenge, existing surrogate models are typically based on either simulation or experimental data. Surrogates trained using numerical simulation data reflect modeling assumptions in their accuracy. Conversely, surrogates developed using experimental data would demand a substantial investment of effort and resources to generate adequate data for training. As a possible solution, in this paper, a multi-fidelity Gaussian process (MFGP) framework is developed to fuse experimental and simulation data. Experimental melt pool dimensions are obtained from single-layer, single-track deposits fabricated via powder-fed L-DED. These constitute the high-fidelity/ground truth data for the MFGP surrogate. An analytical Eagar-Tsai model is calibrated and queried at sampled input points within a predefined process parameter window to yield near-accurate estimates of low-fidelity melt pool data at a significantly lower effort. Thereafter, the MFGP surrogate is designed on the combined sources of data using an appropriate kernel and adequate calibration. An elaborate assessment of the trained surrogate on unseen experimental data is shown to yield results with high accuracy and low uncertainty. The proposed method of blending experimental data with simulation data has the potential to reduce the volume of experimental data required for creating surrogates without compromising accuracy. While the current work focuses on the L-DED of a popular alloy, SS316L, it can be easily extended to other advanced manufacturing processes to design reliable surrogates.

Data-driven density prediction of AlSi10Mg parts produced by laser powder bed fusion using machine learning and finite element simulation

Powder bed fusion of metals using laser beam (PBF-LB/M) is a commonly used additive manufacturing process for the production of high-performance metal parts. AlSi10Mg is a widely used material in PBF-LB/M due to its excellent mechanical and thermal properties. However, the part quality of AlSi10Mg parts produced using PBF-LB/M can vary significantly depending on the process parameters. This study investigates the use of machine learning (ML) algorithms for the prediction of the resulting part density of AlSi10Mg parts produced using PBF-LB/M. An empirical data set of PBF-LB/M process parameters and resulting part densities is used to train ML models. Furthermore, a methodology is developed to allow density predictions based on simulated meltpool dimensions for different process parameters. This approach uses finite element simulations to calculate the meltpool dimensions, which are then used as input parameters for the ML models. The accuracy of this methodology is evaluated by comparing the predicted densities with experimental measurements. The results show that ML models can accurately predict the part density of AlSi10Mg parts produced using PBF-LB/M. Moreover, the methodology based on simulated meltpool dimensions can provide accurate predictions while significantly reducing the experimental effort needed in process development in PBF-LB/M. This study provides insights into the development of data-driven approaches for the optimization of PBF-LB/M process parameters and the prediction of part properties.

Influence of recoil pressure, mushy zone flow resistance and reflectivity on melt pool shape in laser powder bed fusion simulation

Melt pool dimensions (depth: D and width: W) are strongly correlated with defects generated in laser powder bed fusion. The melt pool dimensions have been evaluated by CFD simulation including solid–liquid–gas phase change and laser multiple reflection. Such a multi-physics simulation requires a large number of parameters. Preliminary simulations for parameter identification require a lot of time and effort. The parameter sensitivity to the melt pool dimensions is important; however, the systematic evaluations have hardly been found in the literature. In this study, we performed systematic parametric evaluation for three parameters related to recoil pressure, flow resistance in solid–liquid mushy zone and Fresnel laser reflection. As a result, the D values increased with increasing recoil pressure, but the W values did not. The flow resistance force influenced the velocities in the mushy zone but not the melt pool dimensions. The D increased with increasing reflectivity, but the W did not. The melt pool dimensions varying recoil pressure, flow resistance force and reflectivity were all inconsistent with the experimental melt pool dimensions. In order to agree with the experimental melt pool dimensions, the problems to be solved were discussed by comparing the previous studies.

Impact of track length, track shape, and track location on thermal distortion in laser powder bed fusion of IN625: Single laser vs. three lasers

Laser powder bed fusion is a popular additive manufacturing process for creating complex metallic components. However, it has limitations in terms of build rate and component size. One potential solution to overcome these limitations is the utilization of multiple lasers for part fabrication. Nevertheless, thermal distortion poses a significant challenge in this approach. While several process parameters can be optimized to minimize distortion, the scan pattern is a critical factor. This study focuses on the design of efficient scan patterns to improve distortion in multi-laser powder bed fusion parts. Previous research has primarily explored default scan patterns like raster, spiral, and Hilbert. In contrast, this paper takes a fundamental approach by introducing customizable variables such as track length, track angle, and track location. The resulting paths are simulated using a commercially available thermo-mechanical solver to systematically evaluate their impact on melt pool dimensions, temperature evolution, stresses, and distortion. The findings indicate that multi-laser simulations consistently exhibit lower thermal distortion compared to single-laser configurations. Longer track lengths result in higher maximum displacement, while straight paths with a 0° track angle reduce distortion. Additionally, boundary conditions significantly affect distortion, and printing farther away from constrained edges proves beneficial in minimizing distortion. In conclusion, the insights derived from this study can be used to design of intelligent scan paths.

Phase and thermodynamics-informed predictive model for laser beam additive manufacturing of a multi-principal element alloy

The complex solidification cycles experienced by multi-principal element alloys (MPEAs) during laser-based additive manufacturing (LBAM) often lead to structural defects that affect the build quality. The underlying thermal processes and phase transformations are a function of the process parameters employed. With a moving Gaussian heat source to mimic LBAM and leveraging material thermodynamics guidelines from CALculation of PHAse Diagrams (CALPHAD), we estimate the temperature-dependent thermal properties, phase fractions, and melt pool geometry using an experimentally validated computational fluid dynamics model. The results substantiate that the peak temperatures are inversely correlated to the scan speeds, and the melt pool dimensions can assist in the predictive selection of process parameters such as hatch distance and layer thickness. A relatively low cooling rate recorded during the process is ascribed to the preheating of the substrate to ensure printability of the alloy.

Elucidating the role of pulse shaping on defects formation in aluminum alloys fabricated by powder bed fusion

The Powder Bed Fusion (PBF) additive manufacturing technique, although widely studied and implemented in the industry, still presents issues of porosity and lack of fusion even when optimal processing parameters are used. The benefits and drawbacks of PBF utilizing pulsed lasers are both consequences of the transient temperature field induced by laser-matter interaction. During laser processing with nanosecond pulse durations, intense heating and cooling rates are induced, setting the melt pool dimensions, which are directly related to the occurrence of lack of fusion, and ablation of the processed material, leading to recoil pressure that induces the keyhole effect, increasing the occurrence of porosity. Consequently, controlling the transient temperature field is a major challenge in incorporating pulsed wave laser technology into PBF to minimize porosity and lack of fusion defects. In this study, to reduce porosity and lack of fusion, a smooth heating rate able to sinter powder grains is designed to facilitate heat flow through the heat affected zone, followed by a sharper heating rate that generates a fully molten region while reducing ablation at the same time. This work represents the first attempt to control the transient temperature field, by shaping a nanosecond laser pulse in the time domain, thereby affecting the formation of defects. The effects of the pulse shape on build quality quantified by lack of fusion and high-energy spherical pores were investigated using X-ray microtomography, molecular dynamics (MD), X-ray diffraction (XRD), Electron Backscatter Diffraction (EBSD), and finite element simulations. The variation of porosity and lack of fusion within manufactured samples were revealed by the high-resolution 3D X-ray micro computed tomography. The differences in the transient temperature field, resulting residual stresses, and microstructure are attributed to the differences in the energy delivery rates within the time domain shaped laser pulses. Laser pulse shaping is an additional parameter that plays an important role in optimizing the PBF as-processed part's production opening a new research niche.

A Bézier curve fit to melt pool geometry for modeling additive manufacturing microstructures

This work describes a Metropolis Monte Carlo model for modeling additively manufactured metallic microstructures which employs Bézier curves to formulate melt pool geometry induced by a moving laser heat source. With this formulation, a wide range of melt pool geometries can be modeled and their effects on microstructure morphologies can be evaluated. Melt pool geometries are fit with Bézier curves using melt pool dimensions of width, length, and depth obtained from FEM calculations of the thermal process. Assuming a steady state pool geometry, microstructures are simulated incrementally in the heat affected zone in much the same way an additively manufactured material part is built. The approach is computationally tractable as microstructure simulation is only required in the fusion and heat affected zones, thus requiring less computational resources than would be needed if the entire material/part domain was taken into account. Using the model, microstructures are simulated for LENS™ processed stainless steel. Demonstrations show the model can replicate microstructure morphologies seen in electron beam backscatter diffraction data.

A data-driven ANN model for estimation of melt-pool characteristics in SLM process

The Selective Laser Melting (SLM) process is layered to manufacture a near-net-shaped metallic 3D component. The SLM process involves multiple physical phenomena during part fabrication. A highly intense moving laser heat source causes rapid temperature rise leading to melting and flow of raw material in the melt-pool and subsequent solidification during the process. The present study develops an analytical model by considering various physical phenomena of the SLM process, such as heat transfer, fluid flow, the Marangoni effect, etc. It is solved using an in-house developed analytical formulation based upon Finite Volume Method (FVM) approach. Further, a data-driven machine-learning based on an Artificial Neural network (ANN) model with feedforward neural architecture was trained to estimate the melt-pool dimensions for the single-track SLM process. The developed ANN model was tested for Ti6Al4V alloy by considering all the thermo-physical material properties. A total of 1224 datasets were generated using the analytical formulation to train, validate, and test the neural network. The 70–80% of the training, validation, and testing data fall between the deviation range of 23.55 μm and −21.96 μm. The model predicts melt-pool characteristics with an average error of 0.085%, 0.053%, and 0.056% for depth, width, and length, respectively.

Impact of Melt Flow and Surface Tension on Gap Bridging During Laser Beam Welding

Laser beam welding is an essential technology to enable the transformation to enforce e-mobility. When manufacturing light weight structures like the chassis, precision, speed, quality and low deformation can be expected when using the laser beam as a welding heat source. However, the laser beam is typically used at small dimensions and can fail to transfer its energy to the joining partners when the gap between them becomes large. Beam shaping technologies have developed in the last years to be flexibly used for high-power processes and provide an opportunity to alter the energy input and thereby improve the welding quality and gap bridgability. In this work, multi-spot beam shaping was analyzed using up to nine spots. Experiments were performed using different beam shapes in order to redistribute the energy input, recording the process using high-speed imaging for detection of melt pool dimensions. Those were used as input for a simplified analytical model predicting the process collapse based on the available melt material. Several beam shapes created melt pools that support the material availability behind the keyhole(s). Numerical simulations showed that directed melt flows induced by the keyhole(s) can increase the gap bridgability.

Semi-supervised multi-label feature selection algorithm for online monitoring of laser metal deposition manufacturing quality

Laser metal deposition (LMD) manufacturing, as a technique of laser metal additive manufacturing, has the advantages of short production periods, good economy and unrestricted forming shapes in the direct manufacturing of metal parts. However, the poor accuracy of single-object monitoring and the lack of labelled data make quality monitoring a huge challenge. Accordingly, this study extracts multi-features from the multi-object, including melt pool dimensions, spatters and temperature field, and proposes a semi-supervised multi-label feature selection (SSMLFS) algorithm. For SSMLFS algorithm, a multiple regression quality model is firstly proposed to produce different quality levels for unlabelled data based on microstructure. Then, to achieve feature dimension reduction and filter out highly relevant features, a quality correlation evaluation function is developed to calculate the contribution and ranking of the various features. Moreover, a local search algorithm based on quality is designed to improve the search speed of feature subsets and speed up the convergence of the SSMLFS algorithm. Experimental validation is conducted on BeAM Magic 800 machine and uses several commonly monitoring algorithms based on machine learning. Related experimental results prove that SSMLFS can significantly improve the accuracy and efficiency of quality monitoring compared to single-feature and full-feature monitoring. The proposed SSMLFS algorithm provides a semi-supervised feature selection framework to handle the low accuracy of single-feature monitoring and the complexity of multi-feature monitoring in the quality monitoring of LMD manufacturing.

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.

Optimization of powder catchment efficiency for industrial laser cladding processes

Laser clad overlays provide enhanced corrosion resistance and wear performance to components across a wide range of industries. Powder fed laser processes provide flexibility in material selection, however have traditionally yielded lower material usage efficiency (60–80%) compared to wire fed processes. A general model was developed to predict powder catchment efficiency based on the overlap between the molten pool and powder cloud distribution. Mathematical techniques of asymptotic analysis and blending were used to obtain closed-form expressions relating tabulated material properties and the key process inputs with the critical melt pool dimensions of leading length (xf) and maximum width (y m). An improved experimental technique was implemented to measure the powder cloud distribution, a key process input which previously was not well understood. For baseline cladding conditions, melt pool dimensions were smaller than the powder cloud leading to catchment inefficiency. A custom laser optics module was designed to enable adjustment of the relative position of the melt pool and powder cloud independently from other process parameter changes. Combining optimized parameters with a shift of 1mm, catchment efficiency >90% has been achieved and sustained in production cladding operations. This work is part of a broader program at Apollo-Clad, using simple, accurate, and fast analytical modelling techniques to generate engineering design rules for laser-based processes.