Melt Pool Research Articles

Numerical simulation of single-pass selective laser melting of Mg-Y-Sm-Zn-Zr alloy

A three-dimensional finite volume method (FVM) thermal-fluid coupling model was developed to simulate the temperature field distribution and dimensions of the melt pool in selective laser melting (SLM) of Mg-Y-Sm-Zn-Zr alloy powder. This model aims to accurately determine the temperature field and surface morphology of the melt pool by considering the laser's impact on powder particles, free surface dynamics, convection, and multiphase heat transfer. The study explores how laser power and scanning speed influence temperature distribution, melt pool size, and flow dynamics. The findings show that increasing laser power or reducing scanning speed consistently affects the melt pool size, peak temperature, and cooling rate. During the evaporation of the alloy in the melt pool, fluid flow is primarily governed by recoil pressure and Marangoni convection. Notably, increasing the laser power from 40 W to 100 W and the scanning speed at 0.3 m/s enhances the depth of the melt pool indentation from 13.86 µm to 23.35 µm. Furthermore, the indentation depth correlates positively with the laser line energy density.

In Situ Prediction of Microstructure and Mechanical Properties in Laser-Remelted Al-Si Alloys: Towards Enhanced Additive Manufacturing.

Laser surface remelting of aluminum alloys has emerged as a promising technique to enhance mechanical properties through refined microstructures. This process involves rapid cooling rates ranging from 103 to 108 °C/s, which increase solid solubility within aluminum alloys, shifting their eutectic composition to a larger value of silicon content. Consequently, the resulting microstructure combines a strengthened aluminum matrix with silicon fibers. This study focuses on the laser scanning of Al-Si aluminum alloy to reduce the size of aluminum matrix spacings and transform fibrous silicon particles from micrometer to nanometer dimensions. Analysis revealed that the eutectic structure contained 17.55% silicon by weight, surpassing the equilibrium eutectic composition of 12.6% silicon. Microstructure dimensions within the molten zones, termed 'melt pools', were extensively examined using Scanning Electron Microscopy (SEM) at intervals of approximately 20 μm from the surface. A notable increase in hardness, exceeding 50% compared to the base plate, was observed in the melt pool regions. Thus, it is exemplified that laser surface remelting introduces a novel strengthening mechanism in the alloy. Moreover, this study develops an in situ method for predicting melt pool properties and dimensions. A predictive model is proposed, correlating energy density and spectral signals emitted during laser remelting with mechanical properties and melt pool dimensions. This method significantly reduces characterization time from days to seconds, offering a streamlined approach for future studies in additive manufacturing.

Probabilistic Printability Maps for Laser Powder Bed Fusion Via Functional Calibration and Uncertainty Propagation

Abstract In this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach toward inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to the literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar–Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack-of-fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.

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.

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

Numerical Study of Temperature Field and Melt Pool Properties during Electron Beam Selective Melting Process with Single Line and Multiple Line Scanning

Numerical Study of Temperature Field and Melt Pool Properties during Electron Beam Selective Melting Process with Single Line and Multiple Line Scanning

Microstructure evolution in AlSi10Mg alloy fabricated by laser-based directed energy deposition

Investigation of the influence of processing parameters on the microstructure evolution of the melt pool is important for microstructure control in laser-based directed energy deposition (DED-LB) processes. Single-track experiments of DED-LB AlSi10Mg alloy were conducted in this work, processing with two laser powers (900 and 1800 W) at a fixed scanning speed (20 mm/s). The grain morphology was changed from columnar to equiaxed towards the center of each melt pool. Meanwhile, the morphology of the α-Al cells in the grains varied from elongated to equiaxed along the same direction due to the increase of cooling rate. A more equiaxed microstructure can be obtained in the melt pool produced at a larger laser power. The scales of the microstructures and the grain morphologies in the melt pools were in accord with the microstructure selection map and grain morphology selection map in temperature gradient (G)–growth velocity (V) space constructed by solidification models. Since G and V are directly related to laser power and scanning speed, the G–V maps can provide the guidance for processing optimization to tailor microstructures in DED-LB Al–Si alloys based on the temperature field simulations of melt pools.

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. In this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

In-situ monitoring of the small changes in process parameters with multi-sensor fusion during LPBF

Abstract The small changes in process parameters have significant influences on the stability of laser powder bed fusion (LPBF). Therefore, monitoring the small changes in process parameters is particularly important. This paper proposed a machine learning (ML)-based multi-sensor fusion approach to monitor the LPBF processing state by combining photodiode, acoustic, and visual signals. In order to extract the motion features of the melt pool more accurately and describe its transient changes, an ellipse adjustment algorithm is proposed to segment the melt pool images, eliminating the interference of spatters. The motion features combined with preprocessed acoustic signals and photodiode signals to identify melting states during small changes in process parameters. The proposed ML-based multi-sensor fusion approach achieves impressive prediction accuracies of 99.9% for identifying the fluctuations in the process parameters. The results demonstrate that the proposed method can accurately identify small changes in process parameters, which is of great significance for improving the process stability and providing reliable guidance in subsequent work.

Monitoring of the Weld Pool, Keyhole Morphology and Material Penetration State in Near-Infrared and Blue Composite Laser Welding of Magnesium Alloy

The laser welding of magnesium alloys presents challenges attributed to their low laser-absorbing efficiency, resulting in instabilities during the welding process and substandard welding quality. Furthermore, the complexity of signals during laser welding processes makes it difficult to accurately monitor the molten state of magnesium alloys. In this study, magnesium alloys were welded using near-infrared and blue lasers. By varying the power of the near-infrared laser, the energy absorption pattern of magnesium alloys toward the composite laser was investigated. The U-Net model was employed for the segmentation of welding images to accurately extract the features of the melt pool and keyhole. Subsequently, the penetrating states were predicted using the convolutional neural network (CNN), and the novel approach employing Local Binary Pattern (LBP) features + a backpropagation (BP) neural network was applied for comparison. The extracted images achieved MPA and MIoU values of 89.54% and 81.81%, and the prediction accuracy of the model can reach up to 100%. The applicability of the two monitoring approaches in different scenarios was discussed, providing guidance for the quality of magnesium welding.

A thermal fluid dynamic model for the melt region during the laser powder bed fusion of polyamide 11 (PA11)

Studying the physical characteristics of the melt region in polymer-based laser powder bed fusion (PBF-LB/P) offers good comprehension that is essential for quality control, process optimization, understanding of material behavior, and preserving process consistency. Among the few numerical modelling investigations of PBF-LB/P, this study focuses on 3D multiphysics simulation of melt pool morphologies, beginning with powder bed preparation incorporating the actual particle size distribution using the discrete element method (DEM). A thermal fluid dynamic numerical model is then developed based on the finite volume method (FVM) using Flow-3D Weld to simulate PBF-LB while using polyamide 11 (PA11) as the primary material. The cooling after sintering plays a crucial role for the formation of the melt region due to the high viscosity of the polymer material effectively resulting in creeping flow. Here, we propose a model for both sintering as well as the subsequent cooling step, a two-step model capable of simulating the finished structure accurately with relatively low computational resources. The model's accuracy is verified through a mesh dependency analysis and its predictive power via validation against internal single-track experiments providing dimension data on the melt region width and depth. The simulation results allow for a detailed exploration of the effects of laser settings on melt region morphologies and the occurrence of defects. The model's scope is further extended to multi-track simulation, revealing the connection between surface roughness and laser settings. In general, this study provides significant contributions to the comprehension of PBF-LB/P methodologies which in turn can be used for further process improvement.

Enhancing fusion welding in die-cast Mg alloys: A combined ultrasonic vibration and hot rolling approach for porosity reduction, microstructure refinement, and mechanical property improvement

Severe weld porosity has led the industry to believe that die-cast magnesium alloys cannot be fusion welded. To address this challenge, a new process of ultrasonic vibration coupled with hot rolling was employed to eliminate porosity, and the mechanism associated with weld porosity in die-cast magnesium alloys was analyzed in detail. Unlike conventional welding processes, ultrasonic vibration coupled with hot rolling is effective and efficient. In this study, a roller with ultrasonic vibrations is synchronized with the molten pool to apply pressure to the weld, which is in a semi-solid state on the upper surface. The results showed that hot rolling and ultrasonic vibration coupled with hot rolling could eliminate 78.12 % and 85.5 % of the porosity, respectively. Compared to hot rolling, the ultrasonic vibration coupled with the hot rolling process resulted in smaller and fewer porosities and finer β-Mg17Al12. This is attributed to the cavitation effect of ultrasonic vibration that promotes bubble overflow and breaks the β-Mg17Al12 dendrites in the melt pool, as well as the thermal effect that improves the plasticity of the weld. Furthermore, after closing the porosity under pressure, recrystallized grains will be produced around it. Tensile test results indicated that hot rolling was able to increase the ultimate tensile strength by 84.21 % and elongation by 44.03, whereas ultrasonic vibration coupled with hot rolling was able to increase the ultimate tensile strength by 244.46 % and elongation by 71.70 %. Combining the micron-scale industrial computed tomography results with theoretical calculations, it is found that the porosity of die-cast magnesium alloys is mainly caused by nitrogen, and they originate from gas entrapment during the die-casting process; further, the formation mechanism of die-cast magnesium alloys' fusion-welding porosity is revealed in detail. A physical model of the formation process of welded porosity morphology was established, and the analysis suggested that the pattern of the inner wall of the porosity was related to the Kelvin–Helmholtz instability. This research is expected to improve the weld quality of alloys manufactured by the die-casting process.

Modeling of multi-phase flow in plasma transferred arc cladding of NiCrBSi/WC metal matrix composite

In this paper, a 3D multiphase flow model has been established for the plasma transferred arc (PTA) cladding of composite powder (NiCrBSi/50 wt% WC) on a 40CrNi2MoA substrate. The multiphase flow model simulates both the discrete phase (WC particles) and continuous phase in the melt pool, including the behaviors of heat transfer and particle distribution. The buoyancy, arc pressure, surface tension, and electromagnetic force are calculated. The model is then used to predict the influences of welding currents and preheating temperatures on the depth of heat-affected zone (HAZ), the temperature field, and the dimensions of the cladding layers. To validate this model, the dimensions and the HAZ depth are analyzed and compared to experimental data. The results show that the dimensions and HAZ depth obtained by the model simulation are in good agreement with the experiment. The HAZ depth, the melt pool length, and the dimensions of cladding layer would increase with the increasing welding current or preheating temperature. Driven by the inward convection in the melt pool, WC particles tend to distribute around the center line of cladding layers. This is the first work of modeling 3D multiphase flow in PTA cladding process of particle-reinforced metal matrix composite (MMCs). The work can provide theoretical guidance for adjusting process parameters to control HAZ depth and cladding quality in actual production.

Study on the reduction of residual stress in laser cladding layers through groove texture

In order to develop a method for the production of crack-free cladding layers, we combined surface texturing technology with laser cladding, establishing a multi-field coupled numerical simulation model. A separate investigation was conducted into the temperature, stress, and fluid fields in laser cladding processes with and without texturing, seeking optimal cladding parameters, and conducted experiments. The results of the numerical simulations indicate that pre-set texturing effectively reduces the temperature gradient during the cladding process, thereby making the thermal cycle curve smoother. The residual stresses in the X, Y, and Z directions are reduced by 34.84%, 3.94%, and 50.22%, respectively. The introduction of texturing reduces the internal flow velocity of the melt pool, preventing the occurrence of a double vortex effect. Experimental results show that the residual stresses in the X, Y, and Z directions of the predefined textured cladding layer are reduced by approximately 41%, 8%, and 47%, respectively, compared to the non-textured cladding layer. This effectively improves the surface roughness and internal grain size of the cladding layer, with no significant defects at the metallurgical bonding positions, providing a reference for future improvements in cladding layer quality.

Laser powder bed fusion of CoCrNi composite with high fraction of TiC: The microstructure and mechanical properties at different temperatures

At present, high fraction nano-TiC containing CoCrNi-4wt%TiC composite has been obtained through laser powder bed fusion (LPBF) of the blends of elemental powders and nano-TiC particles. Due to the addition of high fraction nano-TiC particles, there is a significant improvement in the strength of the composite to 1886 MPa at 77K, 1456 MPa at 298K, 528 MPa at 1073K, 300 MPa at 1173K, respectively. Characterized by high scanning speed and high laser power, the laser scan strategy combining is effective in preventing the nano-TiC particles from agglomeration, and the nano-carbides show a heterogeneous distribution with a higher number density and a finer particle size at the bottom of melting pool, the mechanism of which is discussed. The second-phase strengthening of the high-density nano-carbides combining dislocation strengthening or grain boundary strengthening raises the yielding strength of the composite compared to CoCrNi alloy at cryogenic or elevating temperature, respectively. Combining with the deformation-induced nano stack faults/twins plasticity and heterogeneous distribution induced (HDI) strengthening, the addition of high-density nano-carbides contributes to the high tensile strength and good plasticity of the composite at the temperature in a wide range.

Directed energy deposition with a graded multi-material compatibility interface enables deposition of W on Cu

Multi-material Directed Energy Deposition of new high-performance components is limited by phase incompatibilities between metals and by the composition variations which complicate parameter optimization. In this work, a methodology of incorporation of a graded Multi-Material Compatibility Interface (MMCI) was employed to allow deposition of tungsten layers (melting at 3410 °C) on a much less refractory metal, copper (boiling at 2562 °C). The manufacturing of parts combining tungsten and copper is of interest for components designed to dissipate heat in extreme environments, such as the divertor for tokamak fusion reactors. A succession of miscible filler metals rationally selected from thermodynamics were printed by laser and powder injection, with optimization of energy and material inputs for each layer, supported by material characterization and physical numerical analysis of the process phenomenology. 90 %wt. tungsten layer deposition on a copper initial substrate is achieved using an MMCI with 6 intermediate layers and 1.6 mm total thickness. The results reveal the importance of controlling the dilution ratio to achieve stable depositions of multi-material with compositional gradients, with a progressive increase in melt pool temperatures. The numerical analysis highlighted the important role of the dissolution and mixing of partially melted refractory powders, and the selective vaporization of volatile elements.

Insight into the cracking mechanism of super-elastic NiTi alloy fabricated by laser powder bed fusion

ABSTRACT Laser powder bed fusion is considered to be an effective method for forming high-precision NiTi alloys. NiTi alloys formed from Ti-rich or low-Ni content NiTi powders as raw materials exhibit shape memory or trained to be superelastic at room temperature. To fabricate super-elastic NiTi alloys without training, it is often to increase the Ni content of the powder. However, Ni-rich NiTi powders are highly susceptible to cracking during fabrication. Therefore, this study has conducted a systematic investigation and proposed a corresponding process optimisation strategy. The results show that cracking is the result of the combined effect from residual thermal stresses and microstructure during the printing process. The melt pool morphology influenced by the thermal history as well as elemental evaporation determines the direction of crack formation and propagation. We have successfully fabricated room-temperature super-elastic NiTi samples without cracking using high laser power and high scanning speed.

Formation and hydrothermal alteration of a volcanic center: Melt pooling and mass transfers at Langseth Ridge (Gakkel Ridge, Arctic Ocean)

Volcanic centers are characteristic features of ultraslow-spreading mid-ocean ridges, the least-explored parts of the global ridge system. Volcanic centers can provide insights into deep magmatic and metamorphic processes at these ridges. Here, we present data from the largest volcanic center on the Gakkel Ridge, the Langseth Ridge, situated at 60–62°E. Langseth is ∼10 km wide, consisting of three peaks that rise to 585 m water depth, some 3–4 km above the surrounding seafloor. It strikes perpendicular to Gakkel's spreading direction and can be traced for ∼40 km, which translates to an age of ∼8 Myr. Seafloor imaging revealed abundant (pillow) basalt but also fissures and geologic faults across the Langseth Ridge. Basaltic rocks were sampled at all summits and diabase at the slope of the northern summit that dips into the rift valley.Our samples are of normal to depleted mid-ocean ridge basalt composition and exhibit a wide range of major and trace element contents, due to magmatic processes, accumulation of macrocrysts, and hydrothermal alteration. Radiogenic isotope ratios, most notably 143Nd/144Nd and 208Pb/206Pb, trend from typical rift valley compositions to isotopically enriched values with increasing distance to the rift valley. This trend may imply melt pooling from different sources, potentially representing a shift from shallow melting beneath the rift valley to deeper melting of enriched sources and higher degrees of melting underneath Langseth. Mineral compositions and plagioclase sieve textures imply prolonged storage of magma at depth prior to eruption. Hydrothermal alteration occurred over a range of conditions. Basalt from the summits is weakly altered at temperatures ≪100 °C, which likely occurred in situ at the summit sites. Diabase samples experienced chloritization and albitization and display epidote and quartz veins, which formed at >300 °C. These assemblages and temperatures are typical for lower crustal levels and imply uplift of the samples of >1 km. Diabase samples from the Afanasenkov Seamount, another volcanic center on the Gakkel Ridge that we investigated for comparison, were altered under comparable conditions.Our findings suggest a combined volcanic–tectonic origin of the studied volcanic centers, potentially implying that such complexes may generally form due to the interplay of magmatism and tectonics. Researching volcanic centers has the potential to further our understanding of both deep and shallow crustal processes at ultraslow-spreading ridges, providing further insights into the role of these centers as linkages between lithosphere and hydrosphere and the (deep) biosphere they sustain.

MPTP-Net: melt pool temperature profile network for thermal field modeling in beam shaping of laser powder bed fusion

MPTP-Net: melt pool temperature profile network for thermal field modeling in beam shaping of laser powder bed fusion

Online Measurement of Melt-Pool Width in Direct Laser Deposition Process Based on Binocular Vision and Perspective Transformation.

Direct laser deposition (DLD) requires high-energy input and causes poor stability and portability. To improve the deposited layer quality, conducting online measurements and feedback control of the dimensions, temperature, and other melt-pool parameters during deposition is essential. Currently, melt-pool dimension measurement is mainly based on machine vision methods, which can mostly detect only the deposition direction of a single melt pool, limiting their measurement range and applicability. We propose a binocular-vision-based online measurement method to detect the melt-pool width during DLD. The method uses a perspective transformation algorithm to align multicamera measurements into a single-coordinate system and a fuzzy entropy threshold segmentation algorithm to extract the melt-pool true contour. This effectively captures melt-pool width information in various deposition directions. A DLD measurement system was constructed, establishing an online model that maps the melt-pool width to the offline deposited layer width, validating the accuracy of the binocular vision system in measuring melt-pool width at different deposition angles. The method achieved high accuracy for melt-pool measurements within certain deposition angle ranges. Within the 30°-60° measurement range, the average error is 0.056 mm, with <3% error. The proposed method enhances the detectable range of melt-pool widths, improving cladding layers and parts.