Laser Powder Bed Fusion Process Research Articles

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.

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.

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’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.

Assessment of local mechanical properties of laser powder bed fused aluminium alloy by non-destructive testing based on FIMEC indentation

Laser powder bed fusion process is a versatile metal additive manufacturing process. Although significant progress has been made so far, there is still limited large-scale adoption of this technique by the industry. The main problems are repeatability and lack of proper knowledge. In this work, an innovative and non-destructive testing methodology, based on flat-top cylinder indentation, was used to define the mechanical properties of laser powder bed fused aluminium alloy to highlight any variations induced by the combination of process parameters, for global characterization, and by the building direction, for local characterization. Results show similar or improved global mechanical properties of the laser powder bed fused specimens when compared to traditional die-casted ones. Indentation tests highlight a local dependence of properties along the building direction in favor of the upper part of the samples.

Tribo-technological features of laser powder bed fusion process: scratch and wear resistance of AlSi10Mg aluminium alloy

Mechanical systems, regardless of their complexity, very often require that different parts must move relative to each other by sliding their surfaces, therefore appropriate tribological properties are needed. This request appears particularly evident for components fabricated through Metal Additive Manufacturing processes, due to their typical high surface roughness. In the current study, the Laser Powder Bed Fusion technique with optimized parameters is used to produce samples made of AlSi10Mg alloy. Their tribo-technological properties are investigated through progressive load scratch and dry ball-on-plate wear tests. Along with a global characterization, a local analysis has been performed to identify any variations induced by the building direction. The friction coefficient and the wear rate are generally higher than as-cast specimens. Finally, local trends suggest that the central parts of the samples average offer higher resistance to wear and scratch than the outer areas.

Laser powder bed fusion in-situ alloying of W-Y alloy: Microstructure, mechanical properties and cracking suppression

The microcracks caused by impurity oxygen are recognized as one of the main challenges in additive manufacturing of tungsten. In this study, yttrium was incorporated into the tungsten matrix to suppress crack formation, and W-Y alloys with minimal cracks were successfully obtained by laser powder bed fusion (LPBF). It is found that during the LPBF process, yttrium combines with impurity oxygen to generate intergranular and intragranular precipitated particles in situ, which effectively suppress cracking behavior due to impurity oxygen. Furthermore, these intracrystalline nanoprecipitate are cubic Y2O3 particles that have formed a semi-coherent interface with the tungsten matrix. These dispersed particles significantly improved the mechanical properties of W-Y alloy. The results offer a corresponding strategy for crack suppression of tungsten alloys in laser additive manufacturing processes, and would provide a benificial additive manufacturing strategy to other refractory alloys.

Characterizing the as-built surface topography of Inconel 718 specimens as a function of laser powder bed fusion process parameters

Purpose The ability to use laser powder bed fusion (LPBF) to print parts with tailored surface topography could reduce the need for costly post-processing. However, characterizing the as-built surface topography as a function of process parameters is crucial to establishing linkages between process parameters and surface topography and is currently not well understood. The purpose of this study is to measure the effect of different LPBF process parameters on the as-built surface topography of Inconel 718 parts. Design/methodology/approach Inconel 718 truncheon specimens with different process parameters, including single- and double contour laser pass, laser power, laser scan speed, build orientation and characterize their as-built surface topography using deterministic and areal surface topography parameters are printed. The effect of both individual process parameters, as well as their interactions, on the as-built surface topography are evaluated and linked to the underlying physics, informed by surface topography data. Findings Deterministic surface topography parameters are more suitable than areal surface topography parameters to characterize the distinct features of the as-built surfaces that result from LPBF. The as-built surface topography is strongly dependent on the built orientation and is dominated by the staircase effect for shallow orientations and partially fused metal powder particles for steep orientations. Laser power and laser scan speed have a combined effect on the as-built surface topography, even when maintaining constant laser energy density. Originality/value This work addresses two knowledge gaps. (i) It introduces deterministic instead of areal surface topography parameters to unambiguously characterize the as-built LPBF surfaces. (ii) It provides a methodical study of the as-built surface topography as a function of individual LPBF process parameters and their interaction effects.

Assessing the Corrosive Effects of Unmelted Particles in Additively Manufactured Ti6Al4V: A Study in Simulated Body Fluid

This study investigates the corrosion behavior of Grade 23 Ti6Al4V alloys produced through laser powder bed fusion (L-PBF) when exposed to simulated body fluid at room temperature, focusing on the role of unmelted particles. This research aims to understand how these microstructural features, resulting from the additive manufacturing process, influence the corrosion resistance of the alloys. It was observed that unmelted particles serve as critical sites for initiating localized corrosion, including pitting, which significantly compromises the material’s overall durability. Electrochemical testing and detailed surface analysis revealed that these particles, alongside other defects such as voids, exacerbate the susceptibility to corrosion in biomedical environments where high material reliability is paramount. Weight loss measurements conducted over exposure periods of 48 h, 96 h, and 144 h demonstrated a progressive increase in corrosion, correlating with the presence of unmelted particles. These findings underscore the importance of optimizing L-PBF processing parameters to minimize the formation of unmelted particles, thereby enhancing corrosion resistance and extending the operational lifespan of Ti6Al4V implants in biomedical applications.

Quantitative revealing the solute segregation behavior at melt pool boundary in additively manufactured stainless steel using a novel processing method for precise positioning by HAADF-STEM

Laser-powder bed fusion (LPBF) enables the fabrication of complex metallic components by manipulating various laser scan strategies to control microstructure and texture. Multiple thermal cycling and rapid solidification lead to non-equilibrium, non-uniform microstructure, and micro-segregation at the melt pool boundary (MPB), whose accurate location is still invisible by transmission electron microscopy (TEM), and quantitative concentration remains imprecise. In this study, we proposed a novel method to make it clear by controlling the crystallographic texture of 316 L stainless steel through unique LPBF processing parameters to obtain a single-crystal-like microstructure of the cellular structures along the laser scanning direction. The accurate location of the track-track MPB is distinguishable by means of the transverse and longitudinal cellular dislocation structures on both sides. The edge-on state of the track-track MPB makes the quantitative concentration analysis precisely using high-angle annular dark-field scanning TEM with energy-dispersive X-ray spectroscopy, which is in good agreement with the Scheil-Gulliver solidification simulations.

Distortion prediction and geometry compensation using modified inherent strain method for additively manufactured Ti-6Al-4V

A laser powder bed fusion (LPBF) process enables the production of intricate geometries for specialized applications demanding high precision in various industries such as medical, aerospace, and automotive. However, substantial thermal gradients in the LPBF process often led to residual stress, resulting in noticeable distortion and potential part failure of fabricated parts. Therefore, the present work investigates distortion prediction and geometry compensation for additively manufactured Ti-6Al-4V components employing a modified inherent strain method. Unlike previous studies, both relaxed distortions from substrate removal and as-built distortion are considered, incorporating specimens with varying geometrical features such as height and thickness. The study aims to comprehensively evaluate the efficacy of the modified inherent strain approach in capturing distortion under different build scenarios and assess the effectiveness of geometrical compensation in minimizing as-built distortion. Overall, we found that numerical models effectively capture relaxed deflection for samples with varied geometrical features, showing reasonable agreement with experimental results. Additionally, the as-built distortion of samples with thicknesses of 5 mm and above is well-predicted, with an average deviation between numerical and experimental results of approximately 0.125 mm. Nonetheless, we noted the challenge in capturing out-of-plane distortion for thin wedges, suggesting avenues for future investigation. Furthermore, geometry compensation reduces maximum distortion from 0.68 to 0.28 mm and average distortion from 0.15 to 0.05 mm. Multiple iterations of compensation yield insignificant differences in distortion reduction, which suggests sufficient distortion alleviation from single compensation for geometries explored in the present work. Overall, the study provides valuable insights into distortion prediction and compensation strategies for additively manufactured Ti-6Al-4V components.

Acoustic emission monitoring of a laser powder bed fusion process

Additive manufacturing (AM) metal parts offers opportunities for various industrial applications. From individual production of spare parts for unique mechanical components to prototyping of complex structures, the possibilities of production using the additive manufacturing process are manifold. One common AM technique is the Laser Powder Bed Fusion (PBF-LB/M) process, where a laser is used to selectively melt metal powder and create the parts layer wise as designed in a model. During manufacturing certain defects like pores, cracks and lack of fusion may be created in the built parts. As AM parts often have complex geometries, a postprocess non-destructive testing is difficult or even not possible. Thus, different optical monitoring techniques are applied to detect flaws during the build process with the aim to detect and repair defects right away during the manufacturing process. However, optical monitoring system require a clear view of the melt pool and quite expensive equipment. Acoustic monitoring by AE sensors attached to the built plate would be a cheaper alternative which also can be used without visual contact to the building chamber. This paper shows a first approach of an AE based process monitoring. The build plate of a PBF-LB/M machine therefore was adapted to hold four AE sensors that are then used to monitor the process. The build process itself creates AE signals caused by the melting and cooling and the mechanical application of powder. The formation of pores and cracks is expected to create additional acoustic emissions that will be distinct from the AE pattern from the printing process. This AE signals can be linked to the different layers of the printed part or in the long run to the laser position. Results from the first tests in a customized LPBF machine will be shown as well as the implementation of AE into the PBF-LB/M machine.

Nanoparticle-enabled additive manufacturing of high strength 6061 aluminum alloy via Laser Powder Bed Fusion

Wrought aluminum alloy AA6061 is widely used in automotive, aerospace, and other industries due to its good properties, including high strength, excellent corrosion resistance, and good weldability. However, when using Laser Powder Bed Fusion (LPBF) for AA6061, hot cracking becomes a serious problem. In this work, AA6061 powder with internally dispersed nanoparticles has been adopted in a LPBF process. Through optimization of printing parameters, components with minimal porosity (less than 0.5 %) have been successfully produced without cracks. Additionally, by employing a chessboard printing strategy to create finely detailed cellular and grain structures, we have achieved significantly enhanced mechanical properties in its as-printed state for AA6061. These components exhibit an impressive yield strength (YS) of 233 MPa and ultimate tensile strength (UTS) of 310 MPa while maintaining a ductility of approximately 10 %. This performance surpasses that of commercial AA6061 and other Al-Si alloys, establishing it as a high-strength material suitable for various applications.

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.