Laser Powder Bed Fusion Process Research Articles

Tribological effects of varying volumetric energy density in additive manufacturing of inconel 718

Abstract This study investigates the novel effects of varying volumetric energy density (VED) ratios on the tribological characteristics of Inconel 718 alloy fabricated using Laser Powder Bed Fusion (LPBF) process. Nine VED ratios were investigated, and samples underwent non-contact surface roughness tests, Vickers microhardness tests, and residual stress analysis. Notably, this study identified specific VED conditions where superior wear resistance was achieved, contributing new insights into optimizing LPBF parameters for tribological performance. Based on preliminary analysis, samples 1, 3, and 8 were selected for ball-on-disc wear testing. Sample 8, fabricated with 330 W laser power, 50 mm s−1 scan speed, 0.10 mm hatch spacing, and 0.05 mm layer thickness, demonstrated superior tribological behaviour with a 32.12% and 52.11% lower friction coefficient and 31% and 29.92% less mass loss compared to samples 1 and 3, respectively. The presence of γ-Ni, γ′-Ni3 (Al, Ti), γ-FCC, and γ′-L12 phases, along with microstructural features such as columnar and cellular dendritic structures, were confirmed via SEM and x-ray diffraction analyses. This study’s novel findings contribute valuable knowledge for optimizing Inconel 718 production via LPBF, particularly in enhancing wear resistance through tailored VED settings.

Atmosphere Effects in Laser Powder Bed Fusion: A Review

The use of components fabricated by laser powder bed fusion (LPBF) requires the development of processing parameters that can produce high-quality material. Manipulating the most commonly identified critical build parameters (e.g., laser power, laser scan speed, and layer thickness) on LPBF equipment can generate acceptable parts for established materials and moderately intricate part geometries. The need to fabricate increasingly complex parts from unique materials drives the limited research into LPBF process control using underutilized parameters, such as atmosphere composition and pressure. As presented in this review, manipulating atmosphere composition and pressure in laser beam welding has been shown to expand processing windows and produce higher-quality welds. The similarities between laser beam welding and laser-based AM processes suggest that this atmosphere control research could be effectively adapted for LPBF, an area that has not been widely explored. Tailoring this research for LPBF has significant potential to reveal novel processing regimes. This review presents the current state of the art in atmosphere research for laser beam welding and LPBF, with a focus on studies exploring cover gas composition and pressure, and concludes with an outlook on future LPBF atmosphere control systems.

Spatter detection and tracking in high-speed video observations of laser powder bed fusion

Purpose In laser powder bed fusion (L-PBF) additive manufacturing, spatter particles are ejected from the melt pool and can be detrimental to material performance and powder recycling. Quantifying spatter generation with respect to processing conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning. Design/methodology/approach A high-speed camera was used to observe the L-PBF process while varying laser power, laser scan speed and scan strategy on a constant geometry on an EOSM290 using Ti-6Al-4V powder. Two separate convolutional neural networks were trained to segment and track spatter particles in captured high-speed videos for spatter characterization. Findings Spatter generation and ejection angle significantly differ between keyhole and conduction mode melting. High laser powers lead to large ejections at the beginning of scan lines. Slow and fast build rates produce more spatter than moderate build rates at constant energy density. Scan strategies with more scan vectors lead to more spatter. The presence of powder significantly increases the amount of spatter generated during the process. Originality/value With the ability to automatically annotate a large volume of high-speed video data sets with high accuracy, an experimental design of observed parameter changes reveals quantitively stark changes in spatter morphology that can aid process development to mitigate spatter occurrence and impacts on material performance.

Enhanced detection of surface deformations in LPBF using deep convolutional neural networks and transfer learning from a porosity model

Our previous research papers have shown the potential of deep-learning models for real-time detection and control of porosity defects in 3D printing, specifically in the laser powder bed fusion (LPBF) process. Extending these models to identify other defects like surface deformation poses a challenge due to the scarcity of available data. This study introduces the use of Transfer Learning (TL) to train models on limited data for high accuracy in detecting surface deformations, marking the first attempt to apply a model trained on one defect type to another. Our approach demonstrates the power of transfer learning in adapting a model known for porosity detection in LPBF to identify surface deformations with high accuracy (94%), matching the performance of the best existing models but with significantly less complexity. This results in faster training and evaluation, ideal for real-time systems with limited computing capabilities. We further employed Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the model’s decision-making, highlighting the areas influencing defect detection. This step is vital for developing a trustworthy model, showcasing the effectiveness of our approach in broadening the model’s applicability while ensuring reliability and efficiency.

A layer-wise melting defects mitigation method in laser powder bed fusion process based on machine learning and fuzzy inference

A layer-wise melting defects mitigation method in laser powder bed fusion process based on machine learning and fuzzy inference

Advancing laser powder bed fusion with non-spherical powder: Powder-process-structure-property relationships through experimental and analytical studies of fatigue performance

This study investigates the multifaceted interdependencies among powder characteristics (i.e., non-spherical morphology and particle size ranging 50-120 or 75-175µm), laser powder bed fusion (L-PBF) process condition (i.e., contouring), post-process treatments (i.e., hot isostatic pressing (HIP) and mechanical grinding) on the pore, microstructure, surface finish, and fatigue behavior of additively manufactured Ti-6Al-4V samples. Microstructure analysis shows a phase transformation α′ → α+β microstructure after HIP treatment (at 899±14 °C for 2h under the applied pressure of 1034±34bar) of the as-built Ti-6Al-4V parts. The findings from pore analysis using micro-computed tomography (μ-CT) show an increase in sub-surface pores when relatively smaller powders are L-PBF processed including contouring. Surface optical profilometry reveals a decrease in surface roughness when fine powder is L-PBF including contouring. Pore analysis conducted through μ-CT reveals that the presence of lack-of-fusion pores within the L-PBF processed coarse powder is more pronounced when compared to the fine powder. Furthermore, HIP treatment does not eliminate these pores. The fracture failure in as-printed parts occurs at the surface, while the combination of HIP and mechanical grinding alters crack initiation to subsurface pore defects. Fractography reveals that HIP and as-built samples followed the facet formation and pseudo-brittle fracture mechanisms, respectively. Fatigue life assessments, supported by statistical analysis, indicate that mechanical grinding and HIP significantly enhanced fatigue resistance, approaching the benchmarks set by wrought Ti-6Al-4V alloy. A fatigue prediction model which considers the surface roughness as a micro-notch has been used.

Prediction of residual stresses in additively manufactured parts using lumped capacitance and classical lamination theory

Several industries are interested in Laser Powder Bed Fusion (L-PBF) Additively Manufactured (AM) metal parts because their designs can be made arbitrarily complex while retaining bulk-type material properties. However, the residual stresses (RS) and distortions caused by the heat gradients inherent to L-PBF processes are detrimental to the structural integrity of the parts and must be taken into consideration during the part design cycle. Predicting the state of stresses in as-built 3D printed parts is a difficult problem that is typically approached with the use of transient thermomechanical Finite Element Models (FEMs). However, the nonlinearities associated with AM processes are difficult to capture in these FEMs without increasing the computational cost of the simulation, limiting their ability to be incorporated into practical design cycles. This work presents a novel analytical framework that combines lumped capacitance nonlinear heat transfer with time dependent classical lamination theory to efficiently and accurately predict RS in as-built L-PBF parts without the need of FEMs. The simulation was compared to Neutron Diffraction (ND) residual strain measurements taken at Oak Ridge National Laboratories (ORNL) as well as Synchrotron X-ray Diffraction (XRD) strain data published by the National Institute of Standards and Technology (NIST). The simulation predictions and the experimental data showed excellent agreement for the in-plane strain directions, and general agreement for the out of plane strain component, highlighting an area where further development can be implemented.

A Parametric Study on the L-PBF Process of an AlSi10Mg Alloy for High-Speed Productivity of Automotive Prototype Parts

The AlSi10Mg alloy has garnered significant attention for its application in laser powder bed fusion (L-PBF), due to its lightweight properties and good printability using L-PBF. However, the low production speed of the L-PBF process is the main bottleneck in the industrial commercialization of L-PBF AlSi10Mg alloy parts. Furthermore, while L-PBF AlSi10Mg alloy exhibits excellent mechanical properties, the properties are often over-specified compared to the target properties of parts traditionally fabricated by casting. To accelerate production speed in L-PBF, this study investigated the effects of process parameters on the build rate and mechanical properties of the AlSi10Mg alloy. Guidelines are proposed for high-speed additive manufacturing of the AlSi10Mg alloy for use in automotive parts. The results show a significant increase in the build rate, exceeding the conventional build rate by a factor of 3.6 times or more, while the L-PBF AlSi10Mg alloy met the specifications for automotive prototype parts. This strategy can be expected to offer significant cost advantages while maintaining acceptable mechanical properties of topology-optimized parts used in the automobile industry.

Micromechanical and Tribological Characterization of Fabricated Ti–6Al–4V Alloy Using Laser Powder Bed Fusion

Abstract In the dynamic era of advanced manufacturing technology, laser powder bed fusion (L-PBF) have gained popularity in different domains due to its capability to build parts from bulk to miniature size with higher efficiency and precision. Ti–6Al–4V, a bio-inert metal alloy, possesses a unique blend of profound mechanical and biocompatibility attributes, making it highly suitable for implant applications. This study reports the fabrication of Ti–6Al–4V alloy for implant application via the L-PBF process. The objective is to enhance the micromechanical and tribological properties of the fabricated Ti–6Al–4V component by identifying the optimal processing conditions. The fabricated component exhibited a maximum hardness of 395.26 HV and a minimum frictional coefficient of 0.3193 at 195 W laser power, 900 mm/s scanning speed, and 70 μm hatching distance. The wear-rate and absorbed wear volume were measured as 1.265 × 10−5 mm3 N−1 min−1 and 0.3162 mm3, respectively, under sliding conditions. At optimal processing state, the printed surface displayed an alpha-phase morphology with homogeneous microstructural features due to uniform melting of powder particles that improved bond strength and minimized defects. This study offers an experimental insight into operational attributes, paving the way for accelerated production of Ti–6Al–4V alloy components using the L-PBF method and tailoring tribological properties to meet specific functional requirements.

Modelling process monitoring data in laser powder bed fusion: A pragmatic route to additive manufacturing quality assurance

Quality assurance remains a significant challenge for laser powder bed fusion and metal additive manufacturing. Despite system manufacturers offering process monitoring as a possible solution, datasets are large and cumbersome with practical use limited without direct comparative data. Model datasets would enable individual build validation, highlight deviations, and facilitate intelligent build planning whereby challenging features or build strategies could be pre-emptively assessed.Herein a pragmatic approach has been developed to model process monitoring data from a commercial system using a relatively simple algorithm. Using a heuristic method, the algorithm response has been fitted to an experimental dataset to derive governing constants and their relationship to key process parameters. Predictability of constants and model fit has been shown to improve with increasing line energy up to a maximum R2=0.8. Algorithm variable trends, supported by corresponding sensitivity analysis, identified two different behavioural regimes. Under low linear energy density (<0.2J/mm) the cumulative spacetime proximity time-weight variable shows a low sensitivity index, characterised by a flat model response reflected in the experimental data. At higher energies (≥0.2J/mm) algorithm variables become more predictable, reflected in stabilising sensitivity indices, as measurements adopt a form characteristic of the cumulative spacetime proximity function.Effectiveness has been demonstrated through presentation of experimental and model data. Refining the methodology to accommodate noise, geometry, and systematic behaviours are identified as key steps to future development. This feasibility study has laid the groundwork for a generalised predictive tool, capable of realising the quality assurance ambitions promised by laser powder-bed fusion process monitoring.

Two-staged attention-based identification of the porosity with the composite features of spatters during the laser powder bed fusion

Porosity is one of the most serious defects in laser powder bed fusion (LPBF). Reducing porosity is essential to improve the mechanical properties of parts in high-end applications. It is found that the spatter dynamic status is closely related to the porosity, giving an idea to identify. In this study, we propose a novel approach for in-situ identification of porosity in the LPBF with spatter features. To achieve efficient and accurate detection, segmentation, and motion tracking of spatters from captured high-speed images during the LPBF process, YOLO and DeepSORT algorithms are employed. Subsequently, a two-staged attention-based recurrent neural network (TARNN) method is proposed to realize the classification of porosity. The input to TARNN consists of both static and dynamic spatter features extracted from every 10 consecutive frames. Leveraging the RNN architecture enables us to effectively exploit temporal information. Moreover, we introduce an attention-aware linear layer and an attention-based RNN to enhance the extraction of representative features related to porosity characteristics. Through the analysis of the attention mechanism, we can explicitly assess the importance ranking of input features, providing a deeper understanding of spatter features. Experimental results demonstrate the superior performance of the proposed TARNN, with an accuracy of 99.50 % at an inference time of 6.5 milliseconds. The proposed method offers a promising avenue for advancing the understanding and characterization of porosity using spatter features in the LPBF process.

Characterisation and prediction of mechanical properties in laser powder bed fusion-printed parts: a comparative analysis using machine learning

ABSTRACT This study investigates the effects of process parameters including scanning strategy, build orientation, and hatching distance on the mechanical properties of AlSi10Mg parts produced by Laser Powder Bed Fusion (L-PBF). The experiment varied these parameters within defined ranges and used statistical analysis to evaluate their impact on tensile strength and ductility. Results showed that scanning strategy had the greatest influence, followed by hatching distance, while build orientation affected anisotropic properties. Microstructural analysis showed clear correlation between process conditions and mechanical strength, thereby showing the underlying mechanisms that govern material behavior. Moreover, Machine learning models, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs), were applied to predict tensile strength and ductility characteristics. RFR and SVR outperformed ANNs, showing high predictive accuracy with limited datasets. These findings emphasize the importance of optimizing L-PBF process parameters to minimize anisotropy and achieve consistent mechanical properties in produced parts.

Machine learning predictions of spatter behavior in LPBF additive manufacturing

The adoption of additive manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (LPBF), has been rapidly increasing in industries requiring high precision and complex geometries. Despite its advantages, LPBF faces challenges related to defects that affect material quality, with spatter formation being a significant concern. Spatters – tiny particles ejected during the printing process – can adversely affect the final product’s integrity by altering surface roughness and contributing to defects. This study introduces a comprehensive approach to predict the ejection velocity and direction of spatter particles using a suite of machine learning (ML) algorithms, including Random Forest, Gaussian Process Regression, Support Vector Machine, Regularized Linear Regressions, Gradient Boosting Trees, and Neural Networks. Our analysis reveals that the Neural Network model outperforms others, achieving prediction accuracies of 97.58% for spatter velocity and 88.22% for ejection direction, thus offering a substantial improvement in understanding and controlling spatter-related defects in LPBF processes. The practical implications of these predictions are profound, enabling manufacturers to adjust AM parameters in real time to minimize defects and enhance product quality. This study not only fills a gap in the current literature by providing a detailed comparative analysis of multiple ML algorithms for spatter ejection prediction but also paves the way for future research into real-time monitoring and control systems in AM.

Mechanical property, corrosion behavior and cytocompatibility of CoCrMo for dental application: A comparative study of cast and laser powder bed fusion

In this work, by employing powders sourced directly from the original ingot for additive manufacturing, we enabled a comparative overview of the performance between CoCrMo manufactured via laser powder bed fusion (LPBF) and those in their original cast condition. Microstructural analysis revealed that the cast (CT) alloy predominantly consisted of coarse grains with distribution of sigma phase, while the LPBF process resulted in a refined grain structure devoid of the sigma phase. The tensile strength tests demonstrated that the LPBF-derived CoCrMo alloy had substantially greater tensile strength, and ductility compared to CT alloy. Corrosion tests indicated superior corrosion resistance in the LPBF alloy, albeit with a lower metal ion release. In vitro assays confirmed that LPBF CoCrMo alloys displayed favorable cytocompatibility. Consequently, it is concluded that the CoCrMo alloy processed through laser powder bed fusion exhibited enhanced mechanical performance and corrosion resistance. These improvements are primarily attributed to the transformation of the original coarse columnar grain structure through the LPBF technique.

A multiscale model for efficiently simulating laser powder bed fusion process with detailed microstructure and mechanical performance results

Numerical simulation is regarded as an important method for studying the mechanisms of additive manufacturing (AM), especially in building a multiscale simulation model. However, the conversion of physical phenomena and parameters across scales is complex, and the number of simulation elements often changes extremely, leading to an inherent conflict between simulation accuracy and efficiency. Therefore, this work introduces a model that balances the need for detailed grain morphology and overall simulation efficiency, to investigate the relationship between process parameters, microstructure, and mechanical performance during Inconel 718 laser powder bed fusion (L-PBF) process. The cellular automata - finite element method (CA-FEM) was used to simulate the melt pool forming and predict the microstructure evolution. Based on the CA result, the tensile process was simulated through crystal plasticity-finite element method (CP-FEM). Specially, the CA-FEM model was simplified and sliced layer-by-layer to reduce simulation time and data storage, supporting parallel computation. The prediction time for the microstructure growth of millions of elements was less than 1.5 h. The CP-FEM model was simplified by reasonably reducing elements in the representative volume element (RVE) model, improving the simulation efficiency by 73 %. The simulation results were validated through EBSD observation and tensile tests, showing good agreement with experimental results, with the final simulation error for mechanical performance not exceeding 10 %. The effects of process parameters on microstructure and mechanical performance were comprehensively discussed. This model supports the optimization of L-PBF process parameters for Ni-based alloys and provides a reference for improving the efficiency of multiscale simulations for other AM processes and materials.

Generalized SmartScan: An Intelligent LPBF Scan Sequence Optimization Approach for Reduced Residual Stress and Distortion in 3D Part Geometries

Abstract Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using an intelligent (i.e., model-based and optimization-driven) approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by: (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers; (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer; (3) providing the optimization in SmartScan with a balance of exploration and exploitation to make it less myopic; and (4) improving SmartScan&amp;#x92;s computational efficiency via model order reduction using singular value decomposition. Sample 3D test artifacts are simulated and printed using SmartScan in comparison with common heuristic scan sequences. Reductions of up to 92% in temperature inhomogeneity, 86% in residual stress, 24% in maximum deformation and 50% in geometric inaccuracy were observed using SmartScan. An approach for using SmartScan for printing complex 3D parts in practice, by integrating it as a plug-in to a commercial slicing software, was also demonstrated experimentally, along with its benefits in significantly improving printed part quality.

Predicting the fatigue life of additively manufactured AlSi10Mg alloy using a physics-informed neural network incorporating continuous damage mechanics

The material characteristics of additively manufactured AlSi10Mg alloy, including the random distribution of process-induced micro defects, microstructual anisotropy, and grain morphologies with complex diversity, poses a significant challenge in accurately predicting its fatigue life, limiting its application in the aircraft field. This work herein introduces a novel approach incorporating a physics-informed neural network (PINN), in which an artificial neural network (ANN) is embedded with a damage mechanics model (CDM) and the critical process parameters of laser powder bed fusion (L-PBF) additive manufacturing. The partial differential equations of the updated CDM model are introduced into the training procedures of the PINN to building a loss function, effectively “teaching” the PINN to learn the physical knowledge. A comparison of the fatigue life prediction results of ANN and the proposed PINN models shows that the PINN model outperforms its counterparts with a 38.71% higher prediction accuracy. The effects of L-PBF process parameters on the fatigue life of as-built AlSi10Mg is examined using both ANN and PINN, proving the better predictive performance and data-physics consistency of the PINN model. The scheme of this work can inform the efficient prediction of fatigue life of additively manufactured alloys and also reverse prediction or fast finding of optimized process parameters for additive manufacturing.

Influence of Solution Treatment Temperatures on LPBF-Produced Inconel 939 Alloy on Mechanical Performance

This study investigates the influence of solution treatment temperatures on the microstructure and mechanical properties of LPBF (Laser Powder Bed Fusion)-manufactured Inconel 939 alloy. The primary objectives are to assess the impact of sub-recrystallization (below recrystallization temperature) and recrystallization temperatures during solution treatment on mechanical performance under different conditions. This study validates the hypothesis concerning carbide redistribution and mechanical performance in LPBF-produced Inconel 939 parts through comprehensive analysis, including microstructure studies and tensile tests. The results indicate that higher heat treatment temperatures lead to significant changes in carbide redistribution and mechanical performance. For instance, at room temperature, the specimen with sub-recrystallization solution treatment temperature exhibited superior mechanical properties compared to both the as-built and recrystallization-temperature solution treatment specimens. However, at elevated temperatures (700 °C), a reversal in mechanical anisotropy was observed, with the vertical axis demonstrating higher tensile strength compared to the horizontal axis for all material states. Furthermore, a microstructural analysis revealed distinct changes in grain structure and precipitate distribution, particularly the migration of carbides from grain boundaries to intragranular regions. These findings underscore the importance of precise control over heat treatment parameters to achieve optimal mechanical behavior. This research provides valuable insights into optimizing LPBF processes for the Inconel 939 alloy, highlighting the need for a meticulous control of the solution treatment parameters to ensure mechanical integrity. The findings contribute to a broader understanding of material-specific responses in additive manufacturing, with significant implications for aerospace applications.

Fabrication of high-performance Al-Cu-Mg matrix composites via laser powder bed fusion: Incorporating advanced materials and lattice structures

The Laser Powder Bed Fusion (LPBF) process for Al-Cu-Mg alloys, such as the 2024 alloy, is fraught with challenges, primarily due to thermal cracking and the complexity of fabricating lattice structures. This study presents a systematic exploration of the LPBF process capabilities for 2024 Al alloy, a representative Al-Cu-Mg alloy, by incorporating cost-effective micron-scale TiCN ceramic reinforcement particles. The introduction of TiCN significantly modifies the consolidation mechanisms of the 2024 Al alloy matrix during LPBF process, effectively eliminating thermal cracking, refining the matrix grain size, and therefore significantly enhancing the mechanical behavior of Al-Cu-Mg alloys. Nonetheless, this study incorporates advanced lattice structure design to the LPBF processing of 2024-5 vol% TiCN composite material. When compared to Cubic and Diamond structures, Schwarz one demonstrates superior compressive strength, which suggests that higher surface area design of the lattice structures may contribute to the improved mechanical properties. This research offers a novel and economical approach to the development of high-performance Al-Cu-Mg materials tailored for LPBF, laying a groundwork for applications in aerospace, automotive manufacturing, and other sectors.

Fatigue behavior of additively manufactured 316L stainless steel: Competition between the effects of defects and microstructure

Laser Powder Bed Fusion (LPBF) process, is becoming more and more widespread in industry. The possibilities of microstructural control offered by this process are an opportunity to study the contribution of the different length scales of microstructure to the fatigue behavior. This paper is devoted to the understanding of this fatigue behavior resulting from the interactions between the process induced defects and the different polycrystal length scales. Two distinct defect − microstructure competition regimes have been identified. The first concerns microstructures containing large Lack of Fusion (LoF) defects. These LoFs drastically reduce the fatigue life, while microstructure has no influence on the fatigue strength. The second regime concerns microstructures containing small defects. A limited effect of the polycrystalline microstructure was revealed. Furthermore, this paper demonstrates that the ratio between damage initiation defect size and grain size, used in literature to describe the defect and microstructure sensitivity of fatigue strength, is not applicable over a wide range of defects and microstructures, such as obtained by additive manufacturing processes. Finally, the comparison between the fatigue behavior of different microstructure and defect features shows that producing a finer microstructure improves fatigue strength despite the presence of a significant defect population.