Selective Laser Melting Machine Research Articles

Predictive tools for the cooling rate-dependent microstructure evolution of AISI 316L stainless steel in additive manufacturing

This study presents a thorough evaluation of advanced predictive tools for the solidification microstructure of AISI 316L stainless steel, taking into account both chemical composition and cooling rate. In order to achieve a broad range of cooling rates comparable to prominent additive manufacturing processes, autogenous laser welds were carried out on the steel plate by means of powderless directed energy deposition (DED) and selective laser melting (SLM) machines. Computational analysis of cooling rates and metallographic investigations revealed that DED welds solidify at moderate rates, exhibiting a primary ferrite (FA) solidification mode. Conversely, SLM welds solidify at significantly higher rates, showing dual solidification modes: initial austenite (A) at the fusion boundary and subsequent ferrite (F) at the interior. The WRC-1992 constitution diagram predicts the FA mode in accordance with the findings in the DED welds. An implicated Schaeffler diagram, proposed for a comparable cooling rate, also predicts the same mode. An artificial neural network model, referred to as ORFN, consistently predicts the variation of ferrite content within the DED welds based on the cooling rate variation. The dual mode observed in the SLM welds aligns with established knowledge regarding the alteration of stainless steel solidification under extremely high cooling rates. While the occurrence of the A mode at the fusion boundary is anticipated according to implicated Schaeffler diagrams for extremely high cooling rates, the transition to the F mode is not addressed explicitly.

Residual Stress Measurements on Rectangular Specimen SS316L using Numerical Computation Software and Selective Laser Melting

This research aimed to predict the residual stress of additively manufactured rectangular specimen using Selective Laser Melting (SLM) by means of non-linear numerical computation based on Thermo-mechanical method (TMM). The procedure starts with the geometrical and material modelling of rectangular specimen with regards to Austenitic Stainless Steel 316L (SS316L) in which the temperature dependent material data properties such as Young’s Modulus, Thermal Expansion Coefficient, Specific Heat Capacity and Thermal Conductivity are taken into account. The next phase consists of numerical computation procedure which is where the specimen is position 60° of inclination angle from the substrate plate. The support structure is to be generated within the lower surface of the specimen in order to avoid material to collapse during printing process. Laser heat source is to be modelled based on the laser beam width, power, efficiency and scanning speed in order for the numerical computation to accurately predict the thermal problem in SLM process. Furthermore, layer parameters used to fabricate the specimen such as hatch distance, hatch scan width and layer thickness are taken into TMM consideration. Similar set-up from numerical computation by means of laser and layer parameters to fabricate SS316L rectangular specimen is utilized in real fabrication process using SLM machine, Renishaw RenAM 500E. After fabrication of the specimen, electropolishing as for the sample preparation for X-Ray Diffraction (XRD) measurement are to be conducted by means of various depth on both sides of the specimen. For the validation procedure, residual stress on every depth is to be analysed and compared with the result from numerical computation. In conclusion, TMM simulation forecasting an acceptable residual stress on SLM product with relative error up to 14% and the computational time taken to predict the residual stress is only 56 minutes. This exploratory research using TMM simulation to predict residual stress on SLM product could benefit metal additive manufacturing (MAM) production as a whole by neglecting expensive trial and error approach.

Recycling selective laser melting alloy powder on cobalt chromium-to-ceramic bond strength

Statement of problemReusing the powder in selective laser melting machines after multiple cycles is a cost-effective procedure for dental laboratories. However, information on the metal-ceramic bond strength of the framework fabricated by using recycled powder is lacking. PurposeThe purpose of this in vitro study was to investigate how the bonding agent and repeated alloy powder reuse affected the metal-ceramic bond strength of cobalt chromium frameworks fabricated by using selective laser melting. Material and methodsFour square and 40-bar-shaped cobalt chromium frameworks were fabricated by selective laser melting. Half were produced by using virgin alloy powder (Group V; nsquare=2, nbar=20), and half with 30-times reused powder (Group R; nsquare=2, nbar=20). The particle size of each powder was measured by using scanning electron microscopy, and its phase composition was characterized by using radiograph diffraction. Each group was divided into 2 subgroups (Group W [Wash Opaque] and Group N [NP-Bond]) according to the brand of bonding agent used. After ceramic application, the metal-ceramic bond strengths were evaluated by using 3-point bend tests. The bonding agents’ chemical composition was analyzed by using radiograph fluorescence. Bond strength data were analyzed by using a 2-way analysis of variance (α=.05). ResultsMean ±standard deviation bond strengths did not differ significantly (P>.05) between Groups V (31.25 ±4.65) and R (30.88 ±4.78). Group W (35.34 ±1.78) had significantly higher bond strength than Group N (26.80 ±1.74; P<.001). Radiograph diffraction analysis found that the phase composition of all powders was similar. The bonding agent in Group W contained cerium, whereas, that in Group N did not. ConclusionsMetal-ceramic bond strength was unaffected by alloy powder reuse. However, the bonding agent brand may affect the bond strength of cobalt chromium frameworks fabricated by using selective laser melting.

Predictability assessment of as-built hardness of Ti-6Al-4V alloy fabricated via laser powder bed fusion

Evaluating the predictability of selective laser melting (SLM) process has been a persistent endeavor in the manufacturing community since the technique’s inception. The ability to accurately predict the as-built hardness of fabricated parts is critical to judge its serviceability for specific applications. This investigation aims at achieving such predictability with as little as experimentation possible. In this research, reliable historical SLM data was mined on as-built Ti-6Al-4V alloy, from technical articles published over the past fifteen years. Three predictive models of Gaussian Process Regression (GPR), Neural Network (NN) and parametric Multiple Linear Regression (MLR) were built for as-fabricated bulk hardness of Ti-6Al-4V alloy manufactured via SLM. Ten-fold cross-validation training of the models involved a substantial 1,284 spatial datapoints of major SLM process parameters such as laser power, scanning speed, hatch spacing, layer thickness and volumetric energy density. Ultimate assessment of hardness predictability was performed by making predictions on ten cubic Ti-6Al-4V test coupons fabricated using a SLM machine. The GPR model stood out amongst other models, yielding a mean absolute prediction error of 6.12 HV over all samples. The overall predictability of these models came out to be in declining order of GPR > NN > MLR. Rudimentary characterization of test coupons revealed variation in observed hardness. Two tiers of robust validations, comprehensive dataset, statistical insights, actual experimental validation and material characterization make this study extremely unique for as-fabricated hardness of SLM-ed Ti-6Al-4V alloy.

Experimental investigations on the material behaviour of SLM fabricated SS316L

In this present study, the microstructure and mechanical behaviour of selective laser melting (SLM)-fabricated SS316L are investigated. A rectangular plate of dimension 100 × 100 × 8 mm is fabricated using the SLM machine. SS316L powder with a size in the range of 20 to 45 µm is used. The SLM parameters selected for fabrication are: laser power 140 W, scan speed 700 mm/s, hatch spacing 0.12 mm, layer thickness 30 µm and laser spot diameter 80 µm. The energy density of the laser source used to fabricate the component is 55 J/mm3. The chemical composition, microstructure, and defects of the SLM-fabricated SS316L are studied using an optical microscope, SEM, and XRD. The hardness, tensile behaviour and fracture strength of the component are investigated to study the mechanical behaviour of the sample. Finally, fracture surfaces of tensile and fatigue specimens are studied using SEM to understand the failure mechanisms of SS316L produced through SLM. The comparison with conventional SS316L revealed that SLM-fabricated SS316L has higher hardness, ultimate strength, yield strength, and reduced elongation % under untreated condition.

Recoater-Induced Distortions and Build Failures in Selective Laser Melting of Thin-Walled Ti6Al4V Parts

Additively manufactured thin-walled structures through selective laser melting (SLM) are of great interest in achieving carbon-neutral industrial manufacturing. However, residual stresses and warpages as well as recoater crashes often occur in SLM, leading to the build failure of parts, especially for large-scale and lightweight geometries. The challenge in this work consists of investigating how the recoater affects the warpage and (sometimes) causes the failure of different thin-walled Ti6Al4V parts (wall thickness of 1.0 mm). All these parts are printed on the same platform using a commercial SLM machine. After the loose powder removal and before the cutting operation, a 3D-scanner is used to obtain the actual warpage of each component. Next, an in-house coupled thermo-mechanical finite element model suitable for the numerical simulation of the SLM process is enhanced to consider the recoater effects. This numerical framework is calibrated to predict the thin-walled warpage as measured by the 3D-scanner. The combination of numerical predictions with experimental observations facilitates a comprehensive understanding of the mechanical behavior of different thin-walled components as well as the failure mechanism due to the recoater. The findings show that the use of a higher laser energy input causes larger residual stresses and warpage responsible for the recoater crashes. Finally, potential solutions to mitigate the warpage and the recoater crashes in the SLM of lightweight structures are assessed using the validated model.

Investigating the accuracy of mandibulectomy and reconstructive surgery using 3D customized implants and surgical guides in a rabbit model

BackgroundThis study aimed to analyze the accuracy of the output of three-dimensional (3D) customized surgical guides and titanium implants in a rabbit model, and of mandibulectomy, reconstructive surgery, and surgical outcome; additionally, the correlation between surgical accuracy and surgical outcomes, including the differences in surgical outcome according to surgical accuracy, was analyzed.ResultsThe output of implants was accurately implemented within the error range (− 0.03–0.03 mm), and the surgical accuracy varied depending on the measured area (range − 0.4–1.1 mm). Regarding surgical outcomes, angle between the mandibular lower borders showed the most sensitive results and distance between the lingual cusps of the first molars represented the most accurate outcomes. A significant correlation was noted between surgical accuracy in the anteroposterior length of the upper borders pre- and postoperatively and the angle between the mandibular lower borders (regression coefficient = 0.491, p = 0.028). In the group wherein surgery was performed more accurately, the angle between the mandibular lower borders was reproduced more accurately (p = 0.021). A selective laser melting machine accurately printed the implants as designed. Considering the positive correlation among surgical accuracy in the mandibular upper borders, angle between the mandibular lower borders, and more accurately reproduced angle between the mandibular lower borders, the angle between the mandibular lower borders is considered a good indicator for evaluating the outcomes of reconstructive surgery.ConclusionTo reduce errors in surgical outcomes, it is necessary to devise a positioner for the surgical guide and design a 3D surgical guide to constantly maintain the direction of bone resection. A fixed area considering the concept of three-point fixation should be selected for stable positioning of the implant; in some cases, bilateral cortical bone fixation should be considered. The angle between the mandibular lower borders is a sensitive indicator for evaluating the outcomes of reconstructive surgery.

Acoustic Emission and Deep Learning for the Classification of the Mechanical Behavior of AlSi10Mg AM-SLM Specimens

In this research paper, the acoustic emission technique and a deep learning framework based on two types of pre-trained CNN models (alexNet and squeezeNet) and a new model are proposed to characterize and classify the mechanical behavior of AlSi10Mg components. Specimens are built in a Selective Laser Melting machine with different bed orientations along X, Y, Z, and 45 degrees. Tensile tests are performed, and AE signals are recorded from these tests. To characterize the elastic and plastic deformation stages, a time-frequency domain analysis was performed using CWT-based spectrograms. Three different categories of damage classification strategies were implemented, and CNN models were trained for each strategy. CNN models including AlexNet, SqueezeNet, and the new model were used. Several training modes were performed to determine the CNN model that can accurately classify AE data. Understanding the minimum set of AE signals needed to train the CNN while having 100% accuracy and understanding the parameters affecting the accuracy of a CNN and the training time for the efficient classification of AE signals are the main objectives of this work. The results obtained demonstrated that the new simplified CNN model proposed can accurately classify the AE signals in a short time compared to AlexNet and SqueezeNet.

Benchmark Test Artifacts for Selective Laser Melting - A Critical Review

Background: Selective laser melting is a best-established additive manufacturing technology for high-quality metal part manufacturing. However, the technology is yet to be accepted widely, especially in critical applications, due to the absence of a thorough understanding of the technology although several benchmark test artifacts have been developed to characterize the performance of selective laser melting machines. Objective: The objective of this paper is to inspire new designs of benchmark test artifacts to better understand the selective laser melting process, and to promote the acceptance of the selective laser melting technology. Method: The existing benchmark test artifacts for selective laser melting are analyzed comparatively, and the design guidelines are discussed. Results: The modular approach should still be adopted in designing new benchmark test artifacts in the future, and task-specific test artifacts may also need to be considered furtherly to validate machine performance for critical applications. The inclusion of the design model in the manufactured artifact, instead of the conformance to the design specifications, should be evaluated after the artifact is measured for the applications requiring high-dimensional accuracy and high surface quality. Conclusion: The benchmark test artifact for selective laser melting is still under development, and a breakthrough of the measuring technology for internal and/or inaccessible features will be beneficial for understanding the technology.

Mechanical properties of 316L stainless steel samples fabricated by selective laser melting and comparison with other manufacturing methods

Selective laser melting (SLM) is an additive manufacturing technique in which a laser beam with a high energy density is used to melt a metal powder substrate. Although this technique has several advantages, including the possibility of fabricating complex metal components quickly, there are concerns about the mechanical properties of the parts produced by the SLM method. This is study aims to ensure the achievement of acceptable mechanical properties including yield stress, tensile strength, and elongation percentage compared to conventional manufacturing methods. For this purpose, samples of 316L stainless steel were printed using the SLM machine. These samples and samples of annealed 316L bar were tested under same conditions and by the same equipment. Despite the large differences in microscopic structure, no significant differences were observed in mechanical properties. Also, the obtained results were compared with the results related to the sample made by the DLD additive manufacturing method, which is similar to SLM in terms of energy source and raw materials. The result represents that the mechanical strength and microhardness of the sample produced by the SLM technique are higher than the other samples, and the elongation percentage is within the desirable range. The yield stress, tensile strength, and elongation are respectively 595Mpa, 696Mpa, and 34.5%, all of which are within the acceptable range required by the standards for such samples. The investigation of the microstructure shows a complete austenitic cellular structure without considerable solidification defects. Overall, the SLM additive manufacturing is a reliable process to produce 316L stainless steel parts in terms of mechanical properties.

Study of anisotropy through microscopy, internal friction and electrical resistivity measurements of Ti-6Al-4V samples fabricated by selective laser melting

PurposeThis paper aims to investigate the microstructural anisotropy of Ti-6Al-4V samples fabricated by selective laser melting.Design/methodology/approachSpecimens are fabricated through a Renishaw AM400 selective laser melting machine. Three microstructures (as-built, 850°C annealed and 1,050°C annealed) and two building orientations, parallel (PA) and perpendicular (PE) to the building platform, are considered. Starting from in-depth microscopic observations and comprehensive electron backscattered diffraction imaging, the study addresses non-conventional techniques such as internal friction and electrical resistivity measurements to assess the anisotropy of the fabricated parts.FindingsMicroscope observations highlight a fine texture with columnar grains parallel to the building direction in the as-built and 850°C annealed samples. Besides, coarse grains characterized the 1,050°C annealed specimens. Internal friction measurements pointed out the presence of internal stress while storage modulus analyses appear sensitive to texture. Electrical resistivity is resulted to be dependent on grain orientation.Originality/valueThe work uses some novel characterization techniques to study the anisotropy and internal stresses of Ti-6Al-4V samples processed by selective laser melting. Mechanical spectroscopy results suitable in this kind of study, as it mimics the operating conditions of the material.

Effect of laser-induced ultrasound treatment on material structure in laser surface treatment for selective laser melting applications

A new mechanism for controlling the microstructure of products in manufacturing processes based on selective laser melting is proposed. The mechanism relies on generation of high-intensity ultrasonic waves in the melt pool by complex intensity-modulated laser irradiation. The experimental study and numerical modeling suggest that this control mechanism is technically feasible and can be effectively integrated into the design of modern selective laser melting machines.

A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines

Additive manufacturing (AM) has been recognised as a promising technology under the context of Industry 4.0, which is reshaping manufacturing paradigms. A prominent type of AM machine is the selective laser melting (SLM) machine, in which several parts may form a job and be produced concurrently. This paper aims to investigate a scheduling problem in an AM system with non-identical parallel SLM machines. Since, in this system, there might be differences in the material types of parts, the required setup time between two consecutive jobs on the relevant machine is dependent on their material types. Accordingly, a bi-objective mathematical model is extended for the problem, considering the makespan and the total tardiness penalty as two objective functions. Due to the high complexity of the problem, an efficient hybrid meta-heuristic algorithm is developed by combining the non-dominated sorting genetic algorithm (NSGA-II) with a novel learning-based local search founded on the k-means clustering algorithm and a regression neural network. The local search enhances the exploitation ability of the NSGA-II while intelligently being taught during the solving procedure. Finally, the superiority of the proposed hybrid algorithm is demonstrated through a computational experiment.

Effect of the Melt Pool Boundary Network on the Anisotropic Mechanical Properties of Selective Laser Melted 304L

Anisotropic mechanical properties are a well-known issue in selective laser melted parts. The microstructure produced by selective laser melting (SLM) is directional, including the solidified melt pool structures and grains. This work investigates the melt pool boundary’s effects on 304L stainless steel’s compressive properties. 304L stainless steel solid cylinders were built using a pulse laser SLM machine in four directions using three hatch angle rotations: 0°, 67°, and 105°. The twelve samples were compression tested, and the results were analyzed. Numerical models were also created with the different hatch angles and directions. The melt pool boundary network (MPBN) in each build was tracked using the model across multiple planes. Results showed that both the hatch angle and build orientation influenced the concentration of melt pool boundaries present in the manufactured samples. A weak negative correlation of compressive strength to the melt pool boundaries’ concentration was also observed, indicating that the melt pool boundary concentration negatively affected the material’s strength. Local anisotropic plastic deformation was also observed in some of the compressed samples. In those samples, it was observed that directions that plastically deformed more also contained higher concentration of the melt pool boundaries.

Modeling Laser Beam Absorption of Metal Alloys at High Temperatures for Selective Laser Melting

Selective Laser Melting (SLM) is a promising technology to fabricate metallic components with complex geometries. However, determining process windows for specific materials and SLM machines by trial‐and‐error experiments is time consuming and expensive. Numerical simulation has been demonstrated as an efficient way to aid the process development. A crucial aspect is the absorption and reflection of the laser by the material. While there exist elaborated ray tracing approaches and absorption models, the most important drawback is to measure a reliable reflection coefficient of the material for different temperatures and phases. In this work, we discuss the application of a laser beam absorption model for SLM of metal alloys in the high temperature regime. The model bases on fundamental physical laws by taking reflection, refraction and absorption into account. The corresponding reflection coefficient is related to other physical quantities, like the electrical resistivity. The computed reflection coefficients are compared with reflection coefficient measurements of liquid NiFe alloys and with SLM of stainless steel 316L and Ti–6Al–4V. Finally, the laser beam absorption model is implemented in SAMPLE2D, a software for simulation of additive manufacturing on the powder scale, and is exemplary applied on single track melting experiments on compact Ti–6Al–4V plates.

Selective Laser Melting of Maraging Steel Using Synchronized Three-Spot Scanning Strategies.

The selective laser melting (SLM) process, a kind of metal additive manufacturing method, can produce parts with complex geometries that cannot be easily manufactured using material removal processes. With increasing industrial applications, there are still issues such as part quality and productivity that need to be resolved. In this study, maraging steel parts fabricated by synchronized three-spot scanning strategies, i.e., lateral spatial (LS) and spatial inline (SiL), are firstly presented. The LS and SiL represent the three-spot offset direction is perpendicular and parallel to the scanning direction, respectively. A laboratory SLM machine equipped with a fiber laser and three-spot module is used to fabricate the maraging steel parts with two scanning strategies, i.e., LS and SiL. The influence of these scanning strategies on the surface roughness, relative density, hardness, molten pool shapes, and microstructures are investigated. The relative density (~99.02%) and surface hardness (~34.0 HRC) are experimentally found to be higher than the SiL by the LS scanning strategy.

Characterization of Ti-48al-2cr-2nb Built by Selective Laser Melting

Selective laser melting (SLM) was applied to TiAl4822. Electron beam melting (EBM) has been a major additive manufacturing process for TiAl4822, but ductility is a technical challenge with EBM. This research investigates the microstructure and the tensile properties of TiAl4822 fabricated by a new SLM machine equipped with a heating unit. The elongation of the SLM specimen was 8.3 times that of the EBM specimen; this was attributable to the γ-phase−based homogeneous fine grains in the SLM specimen.

Investigation of electrical power consumption of an additive process chain and empirical modelling as feature selection for machine learning algorithms

The focus on the fourth industrial revolution and advancements in 3D printing has reignited the need for energy efficient manufacturing. In particular, Selective Laser Melting (SLM), an additive manufacturing process, has garnered wide attention owing to its adaptability in producing lightweight components for metal industries. Reasonable material demand along with environmental and methodical capabilities of SLM machines has opened up an intriguing possibility to examine its power consumption as well as to determine its suitability for energy efficient manufacturing. In addition, the energy demand of SLM machines along with its occupancy time in a factory floor poses challenges to energy supply grid and subsequent effects on energy flexibility. Hence, it is necessary to determine energy demand of SLM process chain. This paper provides an empirical power consumption analysis of an additive process chain and interprets the power utilized by various process steps of an SLM machine.

Simulation-based analysis of the energy demand within an additive subtractive process chain

Additive manufacturing processes, such as Selective Laser Melting (SLM), has become increasingly established in metal-processing industry offering versatile possibilities for producing individualised components or lightweight structures. SLM machines offer ecological and economical potentials due to comparatively low power and material demand. Required pre- and post-processes as sieving and milling, which are used to ensure a stable process and fulfil demands on surface quality and tolerances, are often neglected. Differentiating from this, an approach for analysing the energy demand of an additive-subtractive process chain is presented based on exemplary processes using a combination of empirical models and a geometric physically-based simulation.

Analysis of post-processing influence on the geometrical and dimensional accuracy of selective laser melting parts

PurposeThis study aims to analyze the effect of the different common post-processes on the geometrical and dimensional accuracy of selective laser melting (SLM) parts.Design/methodology/approachAn artefact has been designed including cubic features formed by planar surfaces orientated according to the machine axes, covering all the X-Y area of the working space. The artefact has been analyzed both geometrically (flatness, parallelism) and dimensionally (sizes, distances) from coordinate measuring machine measurement results at three stages, namely, as-built, after sand-blasting and after stress-relieving heat treatment.FindingsResults from the SLM machine used in this study lead to smaller parts than the nominal ones. This effect depends on the direction of the evaluated dimension of the parts, i.e. X, Y or Z direction and is differently affected by the sandblasting post-process (average erosion ratio of 68, 54 and 9 µm, respectively), being practically unaltered by the HT applied after.Originality/valueThis paper shows the influence, from a geometric and dimensional point of view, of two of the most common post-processes used after producing SLM parts, such as sand-blasting and stress-relieving heat treatment, that have not been considered in previous research.