Melt Pool Geometry Research Articles

Systematic Investigation into Laser Powder Bed Fusion of Ti-5553 through Single-track and Multi-layer Studies for Tailored Manufacturing Solutions

Laser Powder Bed Fusion (LPBF) is recognized as an appealing fabrication process for producing metallic parts with customized properties. In the current research, a comprehensive approach is employed to systematically correlate single-track and multi-layer fabrication, aiming to generate a reliable process map, assess the effect of process parameters on the properties of LPBF-made Ti-5553, and guide the manufacturing of tailored structures. Based on single-track morphology, melt pool geometry, and multi-layer density, 30 combinations of laser power and scanning speed were categorized into three groups to identify the desirable process parameters. The investigation of single-tracks and multi-layers reveals that deeper melt pools, created with higher energy input, result in a more elongated grain structure, higher α phase content, increased strength and hardness, and reduced ductility. It is observed that achieving higher ductility involves a slight decrease in strength. Specifically, a substantial increase of ∼65% in ductility occurs with only a ∼3.5% reduction in strength. Also, it is found that the volumetric energy density (VED) alone is not sufficient as a design parameter, and the significant process parameters (e.g., laser power and scanning speed) should be considered, as two samples with the same VED yield different properties.

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.

Machine learning analysis for melt pool geometry prediction of direct energy deposited SS316L single tracks

Machine learning analysis for melt pool geometry prediction of direct energy deposited SS316L single tracks

On microstructure development during laser melting and resolidification: An experimentally validated simulation study

Integrating experiment and simulation provides invaluable insights into the critical parameters that determine the microstructure of alloys produced by additive manufacturing. Here, the grain structure formation due to solidification during single pass laser scans (mimicking bead-on-plate single tracks) on a 316L stainless steel is studied in situ inside a scanning electron microscope that is directly integrated with a continuous-wave laser. The grain size distribution before melting is used as an initial condition in a coupled phase-field/thermal multiphysics modeling framework. The predicted resolidified microstructures are found to agree favorably with those observed experimentally for multiple laser powers and scan velocities, indicating the validity of the overall model. Grain morphology is analyzed quantitatively, and the top surfaces are compared between the experiments and simulations. Analysis of the three-dimensional grain shapes predicted by the simulations shows that the length of the major axis of the resolidified grains is sensitive to laser power and scan speeds, while the length of the minor axis is not. Furthermore, the preferential alignment of the major axes of the grains depends on the melt pool geometry.

First step toward designing effective real-time control systems in laser directed energy deposition

In-process monitoring and control are essential for quality assurance and consistency of laser-based directed energy deposition processes. Detection of irregularities during deposition in terms of defects or flaws is based on in situ monitoring of output process parameters such as temperature, melt-pool geometry, or deposition height. The real-time feedback of these output parameters allows the development of control strategies for real-time adjustment of input process parameters, such as laser power or scanning speed, to correct detected deviations from the desired output process parameters. Therefore, criteria such as sensitivity, stability, correlation, trends, and interactions of the input-output process parameters have a direct impact on controller design, establishing, for example, control limits or tolerance ranges of the output parameters. This paper focuses on the study of the characteristics of output process parameters to input process parameters. This research involves analyzing and comparing the deposition of single tracks under various input process parameters, including laser power and scanning speed. Melt-pool geometry and temperature are estimated from a visual camera and a hyperspectral line camera, whereas the final deposition geometry is obtained from a laser triangulation scanner. The results show the linearity between input and output process parameters, the steadiness of the output process parameters, the relation between melt-pool and final deposition, and offer insights to design effective in-process control systems.

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.

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.

Impact of melt pool geometry variability on lack-of-fusion porosity and fatigue life in powder bed fusion-laser beam Ti–6Al–4V

Powder bed fusion-laser beam (PBF-LB) parts experience a significant decline in fatigue performance when process-induced defects are present. In this work, a decline in 4-point bend fatigue life was observed in PBF-LB Ti–6Al–4V coupons fabricated at constant power with increasing scanning velocity and which underwent subsequent stress relief and surface machining. Specifically, the presence of pores that resemble lack-of-fusion (LoF) and a decline in fatigue life were observed at scanning velocities lower than that expected from prior published work. It was hypothesized that this unexpected presence of LoF pores resulted from melt pool geometry variability that was not considered in prior work when the LoF criterion was implemented. Further, these pores can be small in size and infrequent in their occurrence when the melt pool geometry variability is not severe. Such sparse pores are challenging to characterize using conventional 2D characterization methods. This work leverages tall and narrow coupon geometry and high-resolution X-ray micro computed tomography (X-μCT) to capture LoF porosity. The results show that a modified melt pool overlap-based LoF criterion considering melt pool geometry variability captures the unexpected occurrence of LoF pores observed in X-μCT. In addition, the LoF percent metric displays a strongly negative correlation with fatigue performance. The insights from this work provide guidance on characterizing melt pool geometry variability across scan lines to systematically evaluate processing parameters that generate LoF pores, which, in turn, could lower fatigue performance.

A Machine Learning Framework for Melt-Pool Geometry Prediction and Process Parameter Optimization in the Laser Powder-Bed Fusion Process

Abstract This study presents a cost-effective and high-precision machine learning (ML) method for predicting the melt-pool geometry and optimizing the process parameters in the laser powder-bed fusion (LPBF) process with Ti-6Al-4V alloy. Unlike many ML models, the presented method incorporates five key features, including three process parameters (laser power, scanning speed, and spot size) and two material parameters (layer thickness and powder porosity). The target variables are the melt-pool width and depth that collectively define the melt-pool geometry and give insight into the melt-pool dynamics in LPBF. The dataset integrates information from an extensive literature survey, computational fluid dynamics (CFD) modeling, and laser melting experiments. Multiple ML regression methods are assessed to determine the best model to predict the melt-pool geometry. Tenfold cross-validation is applied to evaluate the model performance using five evaluation metrics. Several data pre-processing, augmentation, and feature engineering techniques are performed to improve the accuracy of the models. Results show that the “Extra Trees regression” and “Gaussian process regression” models yield the least errors for predicting melt-pool width and depth, respectively. The ML modeling results are compared with the experimental and CFD modeling results to validate the proposed ML models. The most influential parameter affecting the melt-pool geometry is also determined by the sensitivity analysis. The processing parameters are optimized using an iterative grid search method employing the trained ML models. The presented ML framework offers computational speed and simplicity, which can be implemented in other additive manufacturing techniques to comprehend the critical traits.

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.

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.

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.

Understanding the formation of laser-induced melt pools with both wire and powder feeding in directed energy deposition

Wire-powder fed directed energy deposition (WP-DED) has been applied to produce high-performance metal parts. Compared to DED with the feed of a single filler (either powder or wire), simultaneous WP-DED exhibits more complex interactions between fillers and laser-induced melt pools, such as additional powder collisions by wire, different filler properties, intricated heat transfer paths, etc. At present, there is little mechanistic understanding of the interactions among wire, powder particles, and laser-generated melt pools in the WP-DED. In this work, we discovered the filler-melt pool interactions in a coaxial wire-powder fed DED (CWP-DED) process by in situ experimental characterizations. In particular, we investigated the wire-influenced particle dynamics before entering the melt pool. When in-flight particles interact with the melt pool, the temporal-varied melt pool geometry as well as melt flow dynamics were characterized. The bubble formation and evolution in CWP-DED were also revealed. We found that the extruded wire would affect the trajectory of in-flight particles and reduce their velocity before the entry of melt pool due to the inelastic collision. The melt pool size decreased with powder feed rate; however, the powder feed rate had limited influences on the melt pool oscillation. Four modes of fluid flow were observed in the melt pool, including backward flow, upwards flow to the wire-melt pool interface, rebounded flow, and clockwise flow. At a high powder feed rate, more gas-induced bubbles occurred along the melt pool boundary and wire-melt pool interfaces. This study provides a fundamental understanding of filler-melt pool interactions during the CWP-DED process to advance the development of next-generation, fast-rate additive manufacturing technology.

In situ observation and reduction of hot-cracks in laser additive manufacturing

Cracking during Laser Additive Manufacturing is a problem for many higher-strength aluminium alloys, including AA6061. Here, we used a pulsed laser with ramp-down power modulation to improve the cracking resistance by about 50% compared to the use of a rectangular pulsed laser. Using synchrotron in situ X-ray imaging at 100,000 images s−1, ground truth data was obtained about changes in melt pool geometry, solidification rate, and thermal gradients were calculated. An analytical hot cracking model was developed to show that these changes lead to a decreased hot tear susceptibility. Therefore, laser pulse modulation can be an effective tool to reduce crack susceptibility of alloys. More fundamentally, the results demonstrate that modifying thermal conditions provides a pathway to crack elimination in LAM and the model established in our study sets the foundation for further complex laser manipulation in modifying the printability and resulting mechanical properties of hard-to-process alloys in Laser Additive Manufacturing.

Influence of alloy solidification path on melt pool behavior in additive manufacturing

Numerical models used to study transport phenomena in laser-powder bed fusion processes often rely on assumptions and simplifications to reduce their computational expense. One common simplification is in the description of latent heat evolution during the solid-liquid phase change (i.e., the solidification pathway), justified by the fact that the mushy zone thickness is similar to the numerical grid spacing used for continuum transport models. The lack of resolution of transport phenomena in the mushy zone motivates the use of computationally convenient solidification paths such as linear or sigmoidal relationships over pathways derived from fundamental solidification theory such as equilibrium or Scheil models. In the present work, an uncertainty quantification (UQ) framework is used to analyze the influence of solidification pathway selection on the solidification dynamics and melt pool geometries in laser based additive manufacturing (AM) of IN625. Results show the solidification pathway has a quantifiable influence on the cooling rate at the liquidus isotherm, mushy zone thickness, and solidification time. Due to similarities in the latent heat evolution at the beginning of solidification, the equilibrium and Scheil models predict similar cooling rates near the liquidus isotherm, however the wider freezing range of Scheil leads to a wider mushy zone compared to equilibrium. The non-physical latent heat release profiles of sigmoidal and linear paths lead to significant overpredictions of cooling rates at the liquidus isotherm compared to equilibrium and Scheil. These results indicate that careful consideration should be given to the choice of solidification pathway to ensure reliable model predictions.

A Review on Laser Beam Shaping Application in Laser Powder Bed Fusion

Laser powder bed fusion (L‐PBF) is extensively adopted in the aerospace and automobile industries for producing metallic components. The L‐PBF process involves rapid melting and solidification of metallic powders using a laser source to produce dense parts. Most industrial L‐PBF systems use a fiber‐based laser source that emits a Gaussian beam distribution. However, the conventional Gaussian beam used in L‐PBF processes presents inherent limitations that can impact the quality and properties of the final products. This review aims to provide a theoretical background of laser beam shaping techniques and explores the application of alternative beam shapes in L‐PBF. It investigates how different beam profiles affect the melt pool geometry, process stability, productivity, mechanical properties, microstructure, and surface roughness of fabricated parts. Additionally, it discusses different types of beam‐shaping techniques and elements utilized to generate distinct beam shapes. Furthermore, different constraints and limitations that hinder the full exploitation of beam shaping are critically discussed. By analyzing the impact of beam shaping in L‐PBF, this review provides insight into optimizing the AM process, enhancing part quality, and improving processing performance. It will also help readers to overview the research gaps, challenges, and promising future directions in laser beam shaping applications in L‐PBF.This review comprehensively covers and presents the current research trend of laser beam shaping applications in L‐PBF. It delves into the opportunities, challenges, limitations, and futures prospects of beam shaping applications. Furthermore, presents the methods and techniques used to modulate the laser beam profile, including spatial and temporal.This article is protected by copyright. All rights reserved.

Cross-Sectional Melt Pool Geometry of Laser Scanned Tracks and Pads on Nickel Alloy 718 for the 2022 Additive Manufacturing Benchmark Challenges

The Additive Manufacturing Benchmark Series (AM Bench) is a NIST-led organization that provides a continuing series of additive manufacturing benchmark measurements, challenge problems, and conferences with the primary goal of enabling modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark measurement data. To this end, single-track (1D) and pad (2D) scans on bare plate nickel alloy 718 were completed with thermography, cross-sectional grain orientation and local chemical composition maps, and cross-sectional melt pool size measurements. The laser power, scan speed, and laser spot size were varied for single tracks, and the scan direction was varied for pads. This article focuses on the cross-sectional melt pool size measurements and presents the predictions from challenge problems. Single-track depth correlated with volumetric energy density while width did not (within the studied parameters). The melt pool size for pad scans was greater than single tracks due to heat buildup. Pad scan melt pool depth was reduced when the laser scan direction and gas flow direction were parallel. The melt pool size in pad scans showed little to no trend against position within the pads. Uncertainty budgets for cross-sectional melt pool size from optical micrographs are provided for the purpose of model validation.

Statistical parameterized physics-based machine learning digital shadow models for laser powder bed fusion process

This paper presents a statistical physics-based machine learning model for predicting defects, such as surface roughness and lack-of-fusion porosity, in the laser powder bed fusion of metals (PBF-LB/M) additive manufacturing process. The statistical physics-based model is calibrated and validated against controlled single-track experiments and used for statistical prediction for multi-layer and multi-track cases for PBF-LB/M defects. A mechanistic reduced-order-based stochastic calibration process is introduced to capture the stochastic nature of the melt pool. The calibrated physics-based digital shadow model is demonstrated for predicting the surface roughness of the National Institute of Standards and Technology (NIST) overhang part X4, with a difference of 9.3% compared to the experimental results. By leveraging data obtained from both the physics-based model and experiments, a machine learning model has been trained for fast predictions (inference time of 0.4 ms) with high accuracy (error bound of 6.7%). This model can predict melt pool geometries under various processing conditions, offering a control strategy for the PBF-LB/M process. Further, the trained machine learning model is showcased to demonstrate a control application of melt pool geometries (width and depth) for specific processing parameters. These developed models (physics-based and machine learning) serve as a digital shadow of the PBF-LB/M process, offering predictive capabilities to build a digital twin model for process control, optimization, and online monitoring.

Hybrid Analytical-Numerical Modeling of Surface Geometry Evolution and Deposition Integrity in a Multi-Track Laser-Directed Energy Deposition Process

Abstract Modeling multitrack laser-directed energy deposition (LDED) is different from single-track deposition. There is a temporal variation in the deposition geometry and integrity in a multitrack deposition, which is not well understood. This article employs an analytical model for power attenuation and powder catchment in the melt pool in conjunction with a robust fully coupled metallurgical-thermomechanical finite element (FE) model iteratively to simulate the multitrack deposition. The novel hybrid analytical–numerical approach incorporates the effect of preexisting tracks on melt pool formation, powder catchment, geometry evolution, dilution, residual stress, and defect generation. CPM 9V steel powder was deposited on the H13 tool steel substrate for validating the model. The deposition height is found to be a function of the track sequence but reaches a steady-state height after a finite number of tracks. The height variation determines the waviness of the deposited surface and, therefore, the effective layer height. The inter-track spacing (I) plays a vital role in steady-state height evolution. A larger value of I facilitates faster convergence to the steady-state height but increases the surface waviness. The FE model incorporates the effects of differential thermal contraction, volume dilation, and transformation-induced plasticity. It predicts the deposition geometry and integrity as a function of inter-track spacing and powder feed rate. The insufficient remelting of the substrate or the preceding track can induce defects. A method to predict and mitigate these defects has also been presented in this article.

A novel feature engineering approach for predicting melt pool depth during LPBF by machine learning models

Melt pool geometry is a deterministic factor affecting the characteristics of metal Additive Manufacturing (AM) components. The wide array of physical and thermal phenomena involved during the formation of the AM melt pool, along with the great variety of alloy compositions and AM methods, coupled with the clear influence of multiple process parameters, make it difficult to predict the melt pool geometry under a given set of conditions. Therefore, using Artificial Intelligence (AI) approaches such as Machine Learning (ML) is necessary for accurate predictions. Using a physics-informed feature selection strategy along with the application of atomic features for the first time, this work aims to offer accurately trained models relying on existing high-fidelity data for most common alloys in AM academia and industry, i.e., 316 L stainless steel, Ti6Al4V, and AlSi10Mg. Multiple ML algorithms were trained, and the results revealed that the average R2 and RMSE obtained by the K-fold cross-validation (K = 5) were significantly enhanced when laser and material properties, inspired by the analytical models for AM melt pool geometry, were used as the model features. Removing the excess features and applying atomic features further enhanced the accuracy of the models. As a result, R2 for the XGBoost, CatBoost, and GPR models were 0.907, 0.889, and 0.882, respectively, while the hold-out cross-validation led to 0.978, 0.976, and 0.945, respectively. Furthermore, the results showed that the XGBoost model outperforms the Rosenthal equation. This approach provides a pathway to more accurately predict the properties of metal AM components.