Laser Melting Research Articles

Two-Staged Technology for CoCr Stent Production by SLM

Additive manufacturing of porous materials with a specific macrostructure and tunable mechanical properties is a state-of-the-art area of material science. Additive technologies are widely used in industry due to numerous advantages, including automation, reproducibility, and freedom of design. Selective laser melting (SLM) is one of the advanced techniques among 3D fabrication methods. It is widely used to produce various medical implants and devices including stents. It should be noticed that there is a lack of information on its application in stent production. The paper presents the technological aspects of CoCr stent SLM fabrication, including design of stents and development of regimes for their manufacturing. Physical, chemical, and technological properties of CoCr powder were initially determined. Parametric design of mesh stent models was adopted. A two-stage approach was developed to ensure dimensional accuracy and quality of stents. The first stage involves a development of the single-track fusion process. The second stage includes the stent manufacturing according to determined technological regimes. The single-track fusion process was simulated to assign laser synthesis parameters for stent fabrication. Melting bath temperature and laser regimes providing such conditions were determined. Twenty-seven SLM manufacturing regimes were realized. Dependence of single-tracks width and height on the laser power, exposition time, and point distance was revealed. The qualitative characteristics of tracks imitating the geometry of the stent struts as well as favorable and unfavorable fusion regimes were determined. The results of surface roughness regulating of the stents’ structural elements by various methods were analyzed. Thus, this two-staged approach can be considered as a fundamental approach for CoCr stent SLM fabrication.

Investigation of the 4D Multi-Material 316L/FeNi36 Obtained by Selective Laser Melting

Multi-material can have functional properties, which are not typical for the materials of which they are composed (for instance, shape-changing effect). This can be used in robotics, micromachines, aerospace, and other fields. In this work, the 316L/FeNi36 multi-material produced by selective laser melting was investigated. The results show that the interfacial zone of the multi-material exhibits mixing regions of the two alloys but no defects. The microstructure is constituted by large grains with epitaxial growth, which propagate in a directional manner from the 316L alloy through the interfacial zone to the FeNi36 region. The multi-material sample displays three different zones of chemical composition: the FeNi36 composition zone; the interfacial zone; and the 316L zone. The size of the interfacial zone is approximately 50 µm. The multi-material sample exhibits the presence of three distinct phases: γ-Fe; γ-Fe64Ni36; and α-Fe. The hardness of the FeNi36 zone is approximately 163 HV, followed by an interfacial zone with a hardness of approximately 200 HV and then, the 316L zone with a hardness of approximately 214 HV. Functional tests demonstrate that the shape-changing effect is directly correlated with the variation in the FeNi36 thermal expansion coefficient with temperature. For achieving the most pronounced shape-changing effect, the temperature range of 25–215 °C is more suitable.

4D Printing: Research Focuses and Prospects

As an emerging technology in the field of additive manufacturing, 4D printing is highly disruptive to traditional manufacturing processes. Therefore, it is necessary to systematically summarize the research on 4D printing to promote the development of related industries and academic research. However, there is still an obvious gap in the visual connection between 4D printing theory and application research. We collected 2070 studies from 2013 on 4D printing from the core collection of Web of Science. We used VOSviewer 1.6.20 and CiteSpace software 6.3.3 to visualize the references and keywords to explore focuses and trends in 4D printing using scientometrics. In addition, real-world applications of 4D printing were analyzed based on the literature. The results showed that “tissue engineering applications” is the most prominent focus. In addition, “shape recovery”, “liquid crystal elastomer”, “future trends”, “bone tissue engineering”, “laser powder bed fusion”, “cervical spine”, “4D food printing”, “aesthetic planning” are also major focuses. From 2013 to 2015, focuses such as “shape memory polymers” and “composites” evolved into “fabrication”. From 2015 to 2018, the focus was on “technology” and “tissue engineering”. After 2018, “polylactic acid”, “cellulose”, and “regenerative medicine” became emerging focuses. Second, emerging focuses, such as polylactic acid and smart polymers, have begun to erupt and have become key research trends since 2022. “5D printing”, “stability” and “implants” may become emerging trends in the future. “4D + Food”, “4D + Cultural and Creative”, “4D + Life” and “4D + Clothing” may become future research trends. Third, 4D printing has been widely used in engineering manufacturing, biomedicine, food printing, cultural and creative life, and other fields. Strengthening basic research will greatly expand its applications in these fields and continuously increase the number of applicable fields.

The Effects of Laser Power on the Performance and Microstructure of Inconel 718 Formed by Selective Laser Melting

In this study, the effects of laser power on the microstructure and mechanical properties of Inconel 718 formed by SLM were systematically studied. The results show that with the increase in laser power from 285 W to 360 W, the increase in working temperature in the molten pool promoted the evaporation of gas and the vaporization of the low-melting-point alloy components, and the relative density gradually increased from 99.31% to 99.79%. In addition, with the increase in laser energy density, the microstructure gradually coarsened from columnar dendrites to cellular crystals. The nano-hardness of the material decreased with the increase in laser power. The nano-hardness of four groups of samples from 285 W to 360 W decreased form 3.43 GPa to 2.09 GPa, and the elastic modulus decreased form 205.72 GPa to 199.91 GPa.

Development and research testing of a low thrust on gaseous-propellant rocket engine chamber

The article presents preliminary results of the development and research testing of a rocket engine chamber with a thrust of 100 N operating on fuel components: gaseous oxygen-gaseous hydrogen, designed to be used as the main engine of a small upper stage for launching a payload weighing up to 150 kg into target orbits. The main features of the engine chamber are its fabrication by selective laser melting from 12X18N10T steel powder and its regenerative cooling with gaseous hydrogen. Calculation and experiments confirmed the possibility of regenerative cooling of the chamber in the nominal mode. In addition, the current results of the development of a technique for optical recording of the process of structural material removal from the chamber during tests using different optical filters are presented. It is shown that there is a correlation between the brightness of the obtained frames and the hydrogen flow rate. It is also shown that the afterburning of the removed material particles, in contrast to high thrust engines, occurs mainly in the tail of the jet.

Optimal process parameter determination in selective laser melting via machine learning-guided sequential quadratic programing

Selective Laser Melting (SLM) is a widely used additive manufacturing method with critical processing parameters such as Laser Power, Scanning Speed, Hatch Distance, and Laser Beam Diameter, which significantly impact process quality and mechanical properties of fabricated parts like Relative Density, Hardness, and Surface Roughness. This study investigates an effective approach to modeling the SLM process for predicting and optimizing processing parameters using a combination of Machine Learning (ML) and Sequential Quadratic Programing (SQP). The proposed method aims to predict the mechanical properties of SLM-produced AlSi10Mg parts using Machine Learning techniques and then transform the process into a constrained multiple-objective optimization model employing the SQP algorithm to determine optimal process parameters. Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Support Vector Machine (SVM) algorithms are applied to establish correlations between process parameters and mechanical properties, represented as mathematical functions. A cost function is formulated using these functions and minimized using the SQP method. Statistical analysis is implemented to validate the influence of processing parameters on mechanical properties accordingly. The outcomes of the method are verified through additional experiments and analyses using Micro-CT scanning techniques. The results demonstrate that processing parameters have a significant impact on part properties across different scales, with Micro-CT scanning proving instrumental in validating mechanical properties. Overall, this study establishes that multiple objectives can be efficiently optimized through the integration of ML and SQP methods, complemented by a judicious number of experiments.

In-situ laser powder bed fusion: real-time assessment of residual stress through thermal gradient analysis

In-situ laser powder bed fusion: real-time assessment of residual stress through thermal gradient analysis

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.

Optimization of laser powder bed fusion process parameter for the fabrication of AlSi12 using NSGA‐II and Pareto search algorithm

AbstractAdditive manufacturing, notably laser powder bed fusion (LPBF), excels in producing complex geometries and is widely used in the automotive, aerospace, and naval industries. Laser powder bed fusion enables the creation of components with the required stiffness and strength at a lighter weight than traditional manufacturing methods. Aluminium alloys are particularly promising for laser powder bed fusion in the automotive and aerospace sectors. To enhance the effectiveness of laser powder bed fusion‐produced components, optimized process parameters must be designed for specific materials. This study investigates the influence of processing parameters, scan speed, scan strategy, and hatch space, on the relative density, surface roughness, and microhardness of AlSi12 samples fabricated by laser powder bed fusion. A Taguchi L27 orthogonal array was used to systematically analyze the effects of these parameters. A regression model was developed and evaluated through analysis of variance using signal‐to‐noise (S/N) ratios to identify optimal parameter values. Results indicated that the scan pattern significantly affects relative density, while hatch space impacts surface roughness and microhardness. Optimal solutions were obtained through multi‐objective optimization using the non‐dominated sorting genetic algorithm (NSGA‐II) and Pareto search algorithms. Experimental validation showed average errors of 0.483 % and 0.461 % for NSGA‐II and Pareto search algorithms, respectively.

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Abstract Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100kHz and 20kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

Biological Characterization of Ti6Al4V Additively Manufactured Surfaces: Comparison Between Ultrashort Laser Texturing and Conventional Post-Processing.

Among Additive Manufacturing (AM) technologies, Laser Powder Bed Fusion (LPBF) has made a great contribution to optimizing the production of customized implant materials. However, the design of the ideal surface topography, capable of exerting the best biological effect without drawbacks, is still a subject of study. The aim of the present study is to topographically and biologically characterize AM-produced Ti6Al4V ELI (Extra Low Interstitial)samples by comparing different surface finishing. Vertically and horizontally samples are realized by LPBF with four surface finishing conditions (as-built, corundum-sandblasted, zirconia-sandblasted, femtosecond laser textured). Bioactivity in vitro tests are performed with human osteoblasts evaluating morphology, metabolic activity, and differentiation capabilities in direct contact with surfaces. Scanning electron microscope and profilometry analysis are used to evaluate surface morphology and samples' roughness with and without cells. All tested surfaces show good biocompatibility. The influence of material surface features is evident in the early evaluation, with the most promising results of morphological study for laser texturing. Deposition orientations seem to influence metabolic activities, with XZ orientation more effective than XY. Current data provide the first positive feedback on the biocompatibility of laser texturing finishing, still poorly described in the literature, and support the future clinical development of devices produced with a combination of LPBF and different finishing treatments.

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.

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.

Role of Ti-6Al-4V addition in modulating crack sensitivity, solidification microstructure and mechanical properties of laser powder bed fused γ-TiAl alloys

Solidification cracking is still the primary challenge for laser powder bed fusion (LPBF) without high-temperature preheating of γ-TiAl alloys. Circumventing the peritectic reaction and promoting β-solidification by adding the β-stablizing elements have been proved to be an effective way to suppressing the crack formation. In this work, we attempted a more economic method to realize β-solidification via the addition of Ti-6Al-4V (TC4) pre-alloyed powder. The results showed that the TC4 addition successfully changed the solidification path from L→L+β→L+α→α→α+γ→α2+γ to L→L+β→β→α+β→α2+β/B2. As a result, the solidified microstructure was mainly comprised of primary α2 phase and small amount of retained β/B2 (8.5 %) phase distributed along the molten pool boundary. What's more important was that the TC4 addition led to a 44.6 % decrease in crack density. Contributions to low hot cracking sensitivity were found to mainly origin from the relatively lower cooling rate, higher fraction of low-angle grain boundaries (LAGBs) and smaller dendrite coalescence undercooling induced by the TC4 addition. Besides, the smaller temperature interval (10.63K) in the final solidification stage (0.90<fs < 0.94) for the TC4/TiAl alloy was also conducive to facilitate the liquid feeding and dendrite coalescence.

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 Heat Treatment on the Microstructure and Mechanical Properties of TC4 Alloy Fabricated by Selective Laser Melting

Influence of Heat Treatment on the Microstructure and Mechanical Properties of TC4 Alloy Fabricated by Selective Laser Melting

Process development and heat flow analysis for thin walls made of aluminum using extreme high-speed laser material deposition (EHLA3D)

Extreme high-speed laser material deposition (EHLA) is an adapted variant of directed energy deposition (DED-LB-P/M), also known as laser material deposition, and has been developed for the efficient manufacturing of thin layers with high deposition speeds. With precise control of the energy input into the powder gas jet and the substrate, EHLA allows deposition speeds of up to 200 m/min and weld beads as thin as 25 μm. Advantages include a smaller melt pool and a heat-affected zone, allowing the processing of difficult-to-weld material combinations. The development of EHLA for additive manufacturing (EHLA3D) aims to produce highly customized components with improved structural accuracy compared to standard LMD at increased build rates compared to laser powder bed fusion (PBF-LB/M). A promising application is complex lightweight structures for the aerospace industry. However, there is a lack of systematic investigation on lightweight materials processed with EHLA3D at feed rates &amp;gt;20 m/min. In this work, a specially designed tripod machine (maximum feed rate 200 m/min) was used to investigate the buildup of aluminum in process regimes at 30 m/min. After confirming the existing single-track parameters, the tracks were metallographically examined and checked for pores, cracks, and bonding defects. The process was applied to thin-wall geometries and line energies as well as return-times that were varied. To gain an understanding of process-induced heat development, the process was monitored using thermography. Since the process shows geometry-specific heat flow patterns, guidelines have been developed that enable the buildup using different process adaptions.

Influence of Material, Sterilization, and Disinfection on the Accuracy of Three-Dimensional Printed Surgical Templates: An InVitro Study.

To evaluate the influence of the three-dimensional (3D) printing technology, material, sterilization, and disinfection on the accuracy of guided surgical templates. Fifty printed resin surgical templates were designed and fabricated using a digital light processing 3D printer with a photopolymerizing resin, and 50 printed metal surgical templates were designed and fabricated using a selective laser melting 3D printer with a titanium alloy. Templates from both groups were randomly divided into five subgroups involving different sterilization and disinfection procedures. The group without any sterilization or disinfection procedure served as the control group, whereas the other groups were used as the study groups (hydrogen peroxide gas plasma sterilization, 5% povidone-iodine disinfection, 75% ethyl alcohol disinfection, and steam autoclave sterilization). Implant simulations were performed on the 3D-printed resin models, and postoperative impressions were acquired with scan bodies attached to the implants. All surgical templates were digitally scanned. The root mean square was used to determine and quantify fabrication accuracy and reproducibility, and the definitive and planned implant positions were compared. The printed resin templates exhibited lower fabrication accuracy and reproducibility, as well as higher 3D deviations, after steam autoclave sterilization (p < 0.001); however, the printed metal templates were not affected by the different sterilization or disinfection procedures (p > 0.05). Printed metal surgical templates are viable alternatives for guided implant surgery. Preoperative steam or gas plasma sterilization is recommended, especially for metal templates, as resin templates show deformation and decreased accuracy after steam sterilization. chictr.org.cn number: ChiCTR2400081334.

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.

Mechanical and Corrosion Behaviour in Simulated Body Fluid of As-Fabricated 3D Porous L-PBF 316L Stainless Steel Structures for Biomedical Implants.

Laser powder bed fusion (L-PBF) is one of the most promising additive manufacturing technologies for creating customised 316L Stainless Steel (SS) implants with biomimetic characteristics, controlled porosity, and optimal structural and functional properties. However, the behaviour of as-fabricated 3D 316L SS structures without any surface finishing in environments that simulate body fluids remains largely unknown. To address this knowledge gap, the present study investigates the surface characteristics, the internal porosity, the corrosion in simulated body fluid (SBF), and the mechanical properties of as-fabricated 316L SS structures manufactured by L-PBF with rhombitruncated cuboctahedron (RTCO) unit cells with two distinct relative densities (10 and 35%). The microstructural analysis confirmed that the RTCO structure has a pure austenitic phase with a roughness of ~20 µm and a fine cellular morphology. The micro-CT revealed the presence of keyholes and a lack of fusion pores in both RTCO structures. Despite the difference in the internal porosity, the mechanical properties of both structures remain within the range of bone tissue and in line with the Gibson and Ashby model. Additionally, the as-fabricated RTCO structures demonstrated passive corrosion behaviour in the SBF solution. Thus, as-fabricated porous structures are promising biomaterials for implants due to their suitable surface roughness, mechanical properties, and corrosion resistance, facilitating bone tissue growth.