Melt Pool Depth Research Articles

Investigating Melt Pool Dimensions in Laser Powder Bed Fusion of Nitinol: An Analytical Approach

Nitinol (NiTi) has gained popularity across various industries due to its shape memory and superelastic properties. Recently, additive manufacturing (AM) has been increasingly utilized to produce NiTi components. This study focuses on single‐track nitinol samples fabricated via powder bed fusion using laser beam (PBF‐LB). Investigating the effects of laser power and scanning speed on melt pool dimensions reveals that melt pool width increases linearly with laser power and decreases logarithmically with scanning speed. However, melt pool depth exhibits outliers that deviate from these trends. Three analytical models are evaluated to predict melt pool dimensions, generally aligning with experimental trends. Notably, the Eagar–Tsai model delivers the most accurate predictions for melt pool width, with a mean absolute error of less than 10%, while the Gladush–Smurov model is more reliable for melt pool depth predictions, showing a mean absolute error under 20%. In contrast, the Rosenthal equation yields less reliable results for both dimensions. This suggests that a combined approach utilizing the strengths of both the Eagar–Tsai and Gladush–Smurov models may provide the most accurate predictions for the melt pool profile of NiTi in PBF‐LB.

Mechanical Performance of Laser Powder Bed Fused Ti-6Al-4V: The Influence of Filter Condition and Part Location

Deteriorated filter condition in laser powder bed fusion (L-PBF) systems may negatively impact shield gas flow, causing inadequate spatter particle/plume removal, leading to laser beam attenuation and reduction in melt pool depth, and potentially causing more frequent formation of volumetric defects. This work investigated the effects of filter condition and part location on the micro-/defect-structure and mechanical behavior, including tensile and fatigue, of Ti-6Al-4V parts fabricated by L-PBF. Interestingly, within the manufacturer recommended service intervals, no specific effect of filter condition could be observed on the micro-/defect-structure or the mechanical behavior of the fabricated parts. However, the parts’ defect-structures were affected by their location, with ones located near the center of the build plate having less porosity than the ones located away. Although these defects did not affect the tensile properties, they frequently observed to initiate fatigue cracks; the critical defects sizes were often in the range of a few tens of micrometers. Therefore, their sensitivity to location resulted in the location dependence of the fatigue behavior.

Precontouring as a Tool to Improve the Laser Powder Bed Fusion Printability of Inconel939 Thin Walls

This work investigates the influence of adding a contour scan prior to hatching (precontouring) on the printability of Inconel 939 thin‐wall sections produced by laser powder bed fusion. Walls with thicknesses ranging from 500 to 1500 μm, manufactured with two hatch scanning parameter sets that give rise to different microstructures and very high density in bulk samples of the same alloy, are investigated. It is shown that, while the texture as well as the grain size and shape is only influenced moderately by the contour scanning strategy, precontouring does lead to a significant reduction of the melt pool depth, an effect that is increasingly pronounced with decreasing wall thickness. This work highlights the role of the precontour scan as a heat sink, shifting the normalized enthalpy input toward lower values and thus limiting the accumulation of excess heat. Precontouring is put forward as a scanning strategy to improve the manufacturability of thin sections.

Improving melt pool depth estimation in laser powder bed fusion with metallic alloys using the thermal dose concept

Laser-matter interactions in laser powder bed fusion for metals (LPBF-Ms) significantly impact the final properties of the fabricated components. Critical process parameters, such as the linear energy density (LED), the ratio of laser power to scan speed, modify the energy input and consequently modify the melt pool geometry. LED strongly influences the melt pool cross-sectional profile, which dictates the thermal effects, microstructure, and mechanical properties of the finished part. Recognizing the crucial role of the melt pool in additive manufacturing, researchers have developed predictive models to estimate its dimensions and morphology. These models aid in tailoring part properties, optimizing process parameters, and reducing the number of experimental trials. However, existing models are either computationally expensive or analytically overly simplified for general LPBF-M applications. This study proposes an improved model that incorporates the Rosenthal equation as described by Tang to increase the accuracy of melt pool depth prediction. By using the thermal gradient per unit time, termed the “thermal dose” in this paper, corresponding to the LED value that produces experimental near-semicircular melt pool shapes for each studied material, we can improve the melt pool depth estimation. The trend revealed a good fit across the LED range compared with experimental measurements, suggesting the model’s effectiveness.

Investigating temperature, stress, and residual stresses in laser powder bed fusion additive manufacturing of Inconel 625

Laser powder bed fusion (LPBF) is a pivotal method in metal additive manufacturing, enabling the intricate fabrication of complex components. However, the rapid thermal transitions inherent in LPBF can induce residual stresses, potentially leading to defects like distortions, cracks, and delamination. This research aims to investigate the distribution of temperature and stress during the LPBF manufacturing of Inconel 625, as well as the generation of residual stresses. For this purpose, a three-dimensional finite element (FE) model of the LPBF process was developed to explore the influence of various factors, including the number of layers, deposition region dimensions, and layer thicknesses, on temperature and stress distribution. Additionally, the study thoroughly examined the residual stress occurred on the part related to post-cooling and their variations after substrate separation. The outcomes reveal that the dimension of the deposition region significantly impacts both temperature and the size of the melt pool. The melt pool depth of 91 μm was calculated from the FE models, closely aligning with the experimentally measured value of 84.7 μm. Furthermore, the separation of the substrate has a notable effect on the distribution of residual stresses in the LPBF specimen. For instance, the residual stress at the center of the first layer decreased from 818 MPa after the cooling process to 108 MPa following substrate separation.

Computational modeling of multi-track multi-layer laser directed energy deposition process

This study presents the development of a 3D fully coupled thermomechanical finite element model to evaluate the melt pool geometry, defects, and micro-hardness of multi-track multi-layer deposition in the powder feed laser-directed energy deposition (LDED) technique. Fabrication strategies can significantly affect the melt pool geometry and material behavior of the manufactured components. Understanding the effect of process parameters in a multi-track multi-layer LDED is important. Optimal process parameters are crucial for achieving high-quality deposition in terms of deposition geometry and integrity. The model can aid in mitigating issues such as inadequate inter-track or track-substrate fusion, porosity, cracks, and other defects. A multi-track multi-layer finite element model (FEM) can realistically predict melt pool evolution, mechanical properties, and defects in laser-LDED. This work focuses on the development of a fully coupled thermomechanical model for multi-track multi-layer deposition processes using ABAQUS®. The finite element framework employs Johnson-Cook’s flow stress model for constitutive behavior and the Koistein-Marburger equation for predicting hardness. Two tracks and two layers of SS316L powder are deposited on an SS304 substrate in finite element simulations and experiments. The model has been validated for the melt-pool depth in the substrate, deposition geometry, heat-affected zone, and hardness. It has been observed that there is a ∼10 % error in the prediction of melt pool geometry characterization and a ∼12 % error in hardness as compared to experimental results. The melt-pool width, depth, substrate dilution, and hardness have been characterized as a function of process parameters via simulations and experiments. The model is capable of predicting inter-track and interface defects. In addition, the effect of interlayer cooling on the melt-pool geometry and hardness has been studied.

Melt Pool Changes Characterization in Laser-Processed H11 Hot Work Tool Steel Using Point-by-Point Scanning Mode towards LPBF Process Optimization.

Additive manufacturing techniques employing laser-based metal melting have garnered significant attention within the scientific community. Despite a decade of comprehensive research on the fundamentals of these techniques, there still remain unexplored facets related to heat flux impact on metallic alloys' properties. Particularly, the effects of point-by-point laser operation on melt pool formation in metallic materials still remain unclear. Thus, this study focuses on the implications of laser metal melting, particularly investigating a point-by-point laser mode operation's influence on melt pool formation and its geometry in the phase-transformation-sensitive material H11 hot work tool steel. To examine the melt pool, singular laser tracks with various laser parameters were scanned across H11 sheet metal, which allowed for the elimination of layer-by-layer heat cycles' influence on the melt pool's microstructure. Samples were examined by means of metallography, revealing significant differences in the melt pool's depth, influenced mostly by exposure time rather than volumetric energy density. Heat-affected zone effects were found to have a limited range and thus potentially marginal effects in layer-by-layer manufacturing conditions. At the same time, retained austenite concentrations near fusion lines have been found within melt pools, suggesting potential micro-segregation of the alloying additions. The results present guidelines towards laser melting processes optimization.

Development and Validation of a One‐Dimensional Finite Difference Simulation Scheme for Polymer Laser Powder Bed Fusion with Application to the Effect of the Inter Layer Time

The division of the polymer laser powder bed fusion process polymers (PBF‐LB/P) into temporal regimes originating from laser motion, thermal diffusion, viscous flow, crystallization kinetics, and powder application is exploited by considering only the building direction. The reduction of dimensionality enables fast simulations which are used to investigate the influence of the inter layer time on the final part density. Numerical simulation results are validated by experiments using polyamide 12 (PA 12) with good agreement in terms of part density. It is shown that an inter layer time of 90 s leads to nearly dense PA 12 parts while a time of 45 s leads to less dense parts. The cooling effect of the applied powder is identified as a cause for insufficient densification of the previous layer. The simulation tool is quantitatively validated against experimental results for the surface temperature of PA 12 as function of scan speed and hatch distance, for the melt pool depth of polyamide 6 as function of scan speed and for the melt pool depth of polyetherketoneketone as function of laser power. The presented simulation method enables rapid process parameter adjustment for new polymer materials in the PBF‐LB/P process.

A Robust Recurrent Neural Networks-Based Surrogate Model for Thermal History and Melt Pool Characteristics in Directed Energy Deposition.

In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups.

Digital twins for rapid in-situ qualification of part quality in laser powder bed fusion additive manufacturing

This work concerns the laser powder bed fusion (LPBF) additive manufacturing process. Currently, LPBF parts are inspected post-process using such techniques as X-ray computed tomography, optical and scanning electron microscopy, among others. This empirical build-and-test approach for qualification of part quality is prohibitively expensive and cumbersome. To enable rapid and accurate in-situ qualification of LPBF part quality, in this work, we developed a physics and data-integrated digital twin approach. To demonstrate the approach, Inconel 718 parts of various shapes were manufactured under differing LPBF processing conditions. The process was continuously monitored using in-situ thermal and optical tomography imaging cameras. The part-scale thermal history was predicted using an experimentally validated computational thermal simulation. The simulation-derived thermal history and sensor signatures were used as inputs to a k-nearest neighbor machine learning model. The machine learning model was trained with ground truth porosity and microstructure data obtained from post-process characterization. The approach predicted the onset of porosity, meltpool depth, grain size, and microhardness with an accuracy exceeding 90 % (R2). This work thus takes a critical step towards realizing an in-situ Born Qualified part quality assessment paradigm in LPBF.

Study on bulge structure formation mechanisms of laser remelting in air atmosphere

As a key feature of a part or assembly, surface structure can effectively improve the surface properties of the workpiece. Laser remelting is a promising method for surface structure formation, but sensitive to ambient gases. Therefore, this paper presents a numerical model that incorporates the contribution of air atmosphere on surface bulge structure formation. For this, the model couples a concentration field to reflect the oxygen mass transfer in the melt pool and introduces a semi-empirical function to describe the contribution of oxygen to the surface tension. Based on the model, it discusses the surface structure formation mechanisms, the heat-mass transfer characteristics, the melt flow patterns, and the surface force transient evolutions. The results reveal that the action of air atmosphere on melt flow manifests itself through two mechanisms, namely, the enhancement of inward thermocapillary forces and the introduction of outward solutocapillary forces. Furthermore, combined with the laser remelting experiments on 304 stainless steels, the established model is validated concerning the surface bulge structure size, the melt pool depth, and the oxygen distribution.

Influence of overlapping process on the distribution of Cr element in laser cladding 316L powder on 45# steel substrate

To investigate the influence of the overlapping process on the distribution of Cr during the laser cladding process, a novel three-dimensional melting and solidification model based on the volume-average method coupled with Eulerian-Eulerian multiphase was established to simulate the process of laser cladding on a 45# steel substrate with different overlapping processes of 316 L powders. The reliability of the model was verified by comparing the simulation results with the melt pool morphology and melt flow rate measured by a high-speed camera. The height and depth of melt pool were verified through the cross-sectional metallographic diagram. The deviations of the melt pool height and depth for single-path cladding were 2.49 % and 6.35 %, respectively, while the deviations for overlapping process were 8.26 % and 6.88 %, respectively. The deviations of melt flow rate were 12.8 % for single-path cladding and 10.0 % for overlapping, respectively. The distribution of chromium (Cr) was verified based on the results of energy-dispersive spectroscopy (EDS), which demonstrated the accuracy of the model. The results demonstrate that reducing the overlapping rate during the overlapping process will increase the dilution rate of the second path of the molten cladding layer which in turn will decrease the concentration of Cr in the lapped area. Reverse overlapping, on the other hand, increases the temperature of the molten pool and the flow rate of the melt, resulting in a more homogeneous mixing of the elements within the second path of the cladding layer. This reduces the difference between the elemental concentration in the lapped area and the elemental concentration of the first and second paths of the cladding layer.

Investigation on the characteristics of porosity, melt pool in 316L stainless steel manufactured by laser powder bed fusion

Pores are among the primary defects in 316L stainless steel when fabricated through laser powder bed fusion (L-PBF). To better understand and precisely control the formation behavior of pores in the L-PBF-manufactured 316L stainless steel, effects of L-PBF process parameters on the melt pool and pore characteristics have been investigated. A method utilized for extreme values statistics analysis was also applied to investigate and predict the sizes of pores. Relationships among the count of measurements, the laser energy density, the reduced variate, and the maximum pore size were revealed. The findings showed an overall increase in melt pool depth as the laser energy densities was raised, yet the magnitude of this growth weakened as the laser energy density continued to increase. Conversely, it was discovered that the connection between the width of the melt pool and the density of laser energy exhibited a markedly less distinct correlation. As laser energy density escalated, there was a shift in the melt pool mode from conduction-based to a transitional phase, and upon further elevation of the laser energy density, it transitioned to keyhole mode. To obtain a reliable statistical analysis of pores, at least 40 measurements should be acquired for the estimation of the pore sizes by taking comprehensive consideration of time cost. Beside, in all three melt pool modes, at a fixed observation area, the maximum size of pores generally decreased with the escalation in laser energy densities.

High-speed synchrotron X-ray imaging of melt pool dynamics during ultrasonic melt processing of Al6061

Ultrasonic processing of solidifying metals in additive manufacturing can provide grain refinement and advantageous mechanical properties. However, the specific physical mechanisms of microstructural refinement relevant to laser-based additive manufacturing have not been directly observed because of sub-millimeter length scales and rapid solidification rates associated with melt pools. Here, high-speed synchrotron X-ray imaging is used to observe the effect of ultrasonic vibration directly on melt pool dynamics and solidification of Al6061 alloy. The high temporal and spatial resolution enabled direct observation of cavitation effects driven by a 20.2 kHz ultrasonic source. We utilized multiphysics simulations to validate the postulated connection between ultrasonic treatment and solidification. The X-ray results show a decrease in melt pool and keyhole depth fluctuations during melting and promotion of pore migration toward the melt pool surface with applied sonication. Additionally, the simulation results reveal increased localized melt pool flow velocity, cooling rates, and thermal gradients with applied sonication. This work shows how ultrasonic treatment can impact melt pools and its potential for improving part quality.

Multi-fidelity surrogate with heterogeneous input spaces for modeling melt pools in laser-directed energy deposition

Multi-fidelity (MF) modeling is a powerful statistical approach that can intelligently blend data from varied fidelity sources. This approach finds a compelling application in predicting melt pool geometry for laser-directed energy deposition (L-DED). One major challenge in using MF surrogates to merge a hierarchy of melt pool models is the variability in input spaces. To address this challenge, this paper introduces a novel approach for constructing an MF surrogate for predicting melt pool geometry by integrating models of varying complexity, that operate on heterogeneous input spaces. The first thermal model incorporates five input parameters i.e., laser power, scan velocity, powder flow rate, carrier gas flow rate, and nozzle height. In contrast, the second thermal model can only handle laser power and scan velocity. A mapping is established between the heterogeneous input spaces so that the five-dimensional space can be morphed into a pseudo two-dimensional space. Predictions are then blended using a Gaussian process-based co-kriging method. The resulting heterogeneous multi-fidelity Gaussian process (Het-MFGP) surrogate not only improves predictive accuracy but also offers computational efficiency by reducing evaluations required from the high-dimensional, high-fidelity thermal model. The tested Het-MFGP yields an R2 of 0.975 for predicting melt pool depth. This surpasses the comparatively modest R2 of 0.592 achieved by a GP trained exclusively on high-dimensional, high-fidelity data. Similarly, in the prediction of melt pool width, the Het-MFGP excels with an R2 of 0.943, outshining the GP's performance, which registers a lower R2 of 0.588. The results underscore the benefits of employing Het-MFGP for modeling melt pool behavior in L-DED. The framework successfully demonstrates how to leverage multimodal data and handle scenarios where certain input parameters may be difficult to model or measure.

Inexpensive high fidelity melt pool models in additive manufacturing using generative deep diffusion

Defects in Laser Powder Bed Fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool. Experimental in-situ monitoring of the three-dimensional melt pool physical fields is challenging, due to the short length and time scales involved in the process. Multi-physics simulation methods can describe the three-dimensional dynamics of the melt pool, but are computationally expensive at the mesh refinement required for accurate predictions of complex effects. Therefore, we develop a generative deep learning model based on the probabilistic diffusion framework to map low-fidelity simulation information to the high-fidelity counterpart. By doing so, we bypass the computational expense of conducting multiple high-fidelity simulations for analysis by upscaling lightweight coarse mesh simulations. We demonstrate the preservation of key metrics of the melting process between the ground truth simulation data and the diffusion model output, such as the temperature field, the melt pool dimensions and the variability of the keyhole vapor cavity. We predict the melt pool depth within 3 μm based on low-fidelity input data 4× coarser than the high-fidelity simulations, reducing analysis time by two orders of magnitude.

Microstructure evolution and grain growth characteristics of IN625 in laser surface melting: Effects of laser power and scanning speed

This study investigated the effects of laser power and scanning speed on the microstructure evolution and grain growth characteristics of IN625 in laser surface melting. Laser powers of 400 W, 600 W and 800 W at scanning speeds from 200 mm/min to 800 mm/min were employed to melt the surface of as-cast IN625 using a continuous Yb-doped fibre laser. The microstructure from the surface to the melt pool boundary was characterised by scanning electron microscopy (SEM) and electron backscattered diffraction (EBSD). It was shown that a cellular structure was developed at low energy densities (≤ 240 J/mm2), since low energy densities resulted in more rapid solidification. A coarse grain region was found near the surface after melting and a columnar grain growth region was formed close to the melt pool boundary at increasing laser power. Large angle grain boundaries were eliminated and medium angle grain boundaries exhibited an area fraction of 90 % after laser surface melting. This was due to the fact that the twinned grains were fully melted in the melt pool and no twins were formed after solidification. An analytical approach was proposed to the estimate the melt pool depth and good agreement between experimental and calculated melt pool depth was obtained at laser power of 400 W and 600 W.

Sparse modeling of dominant factors affecting porosity formation in laser powder bed fusion of aluminum alloy

The dominant factors affecting porosity formation in laser powder bed fusion (PBF-LB/M) of an aluminum alloy were investigated through sparse modeling with the cross-sectional pore area ratio as the target variable and the process parameters of PBF-LB/M and the melting and solidification conditions of the alloy as the explanatory variables. A combination of a few explanatory variables that did not significantly increase the mean squared error for the relationship between the measured pore area ratios and the ratios estimated via the regression equations was found through lasso regression and backward elimination, which indicated that the energy density (one of the process parameters) and melt-pool depth (one of the melting conditions) were the dominant factors affecting the pore area ratio. The obtained regression coefficients for the energy density and melt-pool depth were negative and positive, respectively. In addition, the relationship between the energy density and melt-pool depth was curvilinear. These results suggest not only that the pore area ratio increases with the energy density and melt-pool depth but also that it decreases with an increase in the energy density or a decrease in the change rate of the pool depth under the range of the slow increase in the pool depth with an increase in the energy density.

Avoiding heat source calibration for finite element modeling of the laser powder bed fusion process

The melt pool characteristics govern process stability and microstructure development during Laser Powder Bed Fusion (LBPF). The melt pool is strongly affected by the laser parameters and based on the melt pool depth-to-width ratio, three melting modes can be distinguished: conduction, transition, and keyhole mode. Finite element (FE) simulations have been extensively used to investigate the cyclic thermal history of the LPBF process and the effects of different process parameters on the part quality. However, the heat sources applied in such simulations can only accurately predict the melt pool behavior, and thus the temperature profile, when processing in conduction mode. As for the transition and keyhole regime modeling, heat source parameters or material properties are usually adjusted to make the simulated melt pool dimensions match the real values. This study proposes a novel approach to bypass the conventional heat source calibration strategy to simulate the LBPF process across different melting regimes. The predicted melt pool dimensions are validated for a wide range of process parameters applied to three different alloys: Ti-6Al-4 V, 316 L austenitic stainless steel (316 L), and 2507 super duplex stainless steel (SDSS). The average errors obtained on the melt pool width, depth, length, and aspect ratio are 9.7 %, 12.4 %, 14.5 %, and 14.3 % respectively. These results demonstrate how the proposed approach can capture the melt pool dimensions throughout different materials and melting modes eliminating the time-consuming heat source calibration steps and facilitating the LPBF modeling workflow.

The Effect of Scanning Strategy on the Thermal Behavior and Residual Stress Distribution of Damping Alloys during Selective Laser Melting.

The manufacture of damping alloy parts with stable damping properties and high mechanical performances in the selective laser melting (SLM) process is influenced by temperature evolution and residual stress distribution. Choosing an appropriate scanning strategy, namely the specific trajectory along which the laser head scans powders within given area, is crucial, but clearly defined criteria for scanning strategy design are lacking. In this study, a three-dimensional finite element model (FEM) of the SLM process for manufacturing a WE43 alloy component was established and validated against the published experimental data. Eleven different scanning strategies were designed and simulated, considering variables such as scanning track length, direction, Out-In or In-Out strategy, start point, and interlayer variation. The results showed that scanning strategy, geometry, and layer number collectively affect temperature, melt pool, and stress outputs. For instance, starting scanning at a colder part of the powder layer could lead to a high peak temperature and low melt pool depth. A higher layer number generally results in lower cooling rate, a lower temperature gradient, a longer melt pool life, and larger melt pool dimensions. Changing the start point between scanning circulations helps mitigate detrimental residual stress. This work highlights the potential of analyzing various scanning strategy-related variables, which contributes to reducing trial-and-error tests and selecting optimal scanning strategies under different product quality requirements. This article can assist in the design of appropriate scanning strategies to prevent defects such as element loss due to evaporation, poor bonding, and deformation or cracking from high residual stress. Additionally, identifying stress concentration locations and understanding the effects of geometry and layer number on thermal and mechanical behaviors can assist in geometry design.