Search Results (1,484)

Selective laser melting (SLM) is an additive manufacturing method based on powder bed fusion that has gained prominence in prosthodontics for its capability to create intricate, patient-specific metal restorations with precision and consistency. SLM has become an important part of digital dental workflows, allowing for the direct creation of dental frameworks from computer-aided design (CAD), offering advantages over traditional casting and subtractive milling techniques. This review outlines the fundamentals of SLM, the dental alloys commonly employed, and the microstructural characteristics that affect mechanical properties, corrosion resistance, and biocompatibility. It explores current uses in removable partial denture frameworks, fixed dental prostheses, metal–ceramic restorations, implant-supported prosthetics, and maxillofacial rehabilitation. Alloys based on cobalt–chromium and titanium produced through SLM exhibit strong mechanical properties, fatigue resistance, and biological compatibility when suitable post-processing is conducted. Despite these advantages, issues such as surface roughness, porosity, anisotropy, powder handling, and high costs remain, and there is a lack of extensive long-term clinical data. Ongoing process refinement and clinical validation are crucial for the wider integration of SLM into standard prosthodontic practice. Full article

Laser powder bed fusion (LPBF) can fabricate intricate metal components but is prone to defects, such as porosity and cracks, that degrade performance. We present an in situ monitoring framework that fuses structure-borne acoustic emission (AE) and coaxial two-color pyrometry acquired synchronously at 1 MHz. Modality-specific encoders are pretrained separately, their latent representations are exported, and a lightweight feature-level fusion classifier with two binary heads predicts crack-like and porosity-like indications. Evaluation uses a held-out grouped experiment/build-machine-part split with independent Archimedes density and micro-CT ground truth. On the held-out test set, the fused model achieved F1 = 0.974 for crack-like detection and F1 = 0.987 for porosity-like detection, with AUROC = 0.998 and 0.993, respectively. Recall was 1.00 for both heads, corresponding to false-positive rates of 11.18% for crack-like and 0.945% for porosity-like indications. These results support synchronized AE-pyrometry fusion as a promising high-sensitivity in situ screening approach for LPBF. A later matched within-framework ablation campaign was also performed under stricter checkpoint-screening rules to compare AE + PY + Aux, AE + PY, AE-only, and PY-only variants under a common grouped-split protocol. Together, these results support multimodal monitoring while highlighting the need for explicit coupon/geometry-stratified reporting and for separately architecture-optimized unimodal baselines. Full article

Reliable property prediction and process selection in laser powder bed fusion are hindered by small, set-level datasets in which key morphology descriptors are intermittently missing, limiting both generalization and actionable co-design. A hybrid multimodal surrogate strategy is introduced that couples engineered process physics features with morphology proxies through a deployable two-stage embedding module and gradient-boosted tree regressors. Set-resolved inputs are assembled from L-PBF parameters, linear energy density and related energy-density variants, pore and prior-β grain summary statistics, and stress–strain-derived descriptors, followed by missingness-aware feature filtering, median imputation, and 5-fold GroupKFold evaluation grouped by set_id, with morphology embeddings learned on training folds and predicted when absent. Across six targets, the final deployable models achieve an RMSE/R² of 11.07 MPa/0.895 (yield), 13.88 MPa/0.873 (UTS), 0.677%/0.861 (elongation), and 2.38 GPa/0.663 (modulus), while roughness and hardness remain challenging (RMSE 2.31 μm and 16.54 HV; R² about 0.12 and 0.11). These surrogates enable constraint-aware candidate generation that identifies a concise set of manufacturing recipes balancing strength and surface objectives under uncertainty-aware screening. The resulting framework provides a practical blueprint for multimodal, small-data additive manufacturing studies and can be extended to richer microstructure measurements and prospective validation to accelerate functional and biomedical alloy development. Full article

As part of a NASA University Leadership Initiative (ULI) program, this work supports the continued development and evaluation of a fatigue-based process window for stress-relieved Ti-6Al-4V specimens produced via laser powder bed fusion (PBF-LB/M). Four-point bend and axial fatigue specimens were fabricated by NASA ULI collaborators across a range of scan velocities (800–2000 mm/s) at a constant power of 370 W using an EOS M290 system. All fatigue specimens were low-stress-ground by a commercial vendor and tested at Case Western Reserve University (CWRU) under load-controlled cyclic loading at a stress ratio of R = 0.1. This paper presents a curated dataset linking PBF-LB/M process parameters to fatigue outcomes across 175 specimens. Of these, 136 fractured and this study includes fatigue crack initiation site identification and defect morphology metrics derived from post mortem SEM analysis. Specimens that reached runout (10⁷ cycles) and did not fracture under subsequent fatigue testing are retained in the dataset, with fractographic fields marked as ‘NA’ to indicate non-applicability. The dataset includes specimen metadata, processing parameters, fatigue life data, fatigue initiation site classification (e.g., keyhole, gas-entrapped pore (GeP), lack-of-fusion (LoF), contamination), defect size and shape descriptors, and spatial location relative to the free surface. These data are intended to support defect-based fatigue life prediction, probabilistic modeling, process–structure–property studies, and machine learning frameworks linking process parameters to fatigue performance in PBF-LB/M Ti-6Al-4V. Full article

To address the challenges associated with laser powder bed fusion (LPBF) of overhanging structures—namely warping deformation, powder adhesion, and inadequate forming accuracy—this study investigates the optimization of the support–part contact interface using Inconel 625 alloy. The objective is to achieve high-quality part formation with minimal support structures. A Taguchi experimental design was employed to systematically evaluate the effects of key block support parameters—tooth height, tooth top length, tooth base length, and tooth base spacing—on the forming performance of overhanging structures, with forming accuracy and support removability as the optimization targets. The results reveal that tooth top length significantly influences both the forming accuracy of overhanging specimens and the ease of support removal. Specifically, an increase in tooth top length leads to a rapid reduction in specimen deformation, but simultaneously increases the difficulty of support removal. When the tooth top length was set to 0.1 mm, all overhanging specimens failed to form successfully. Tooth base length also plays a critical role in support removability, with removal difficulty initially decreasing and then stabilizing as the tooth base length increases. Based on the trade-off between forming quality and support removability, the optimal parameter combination was identified as: tooth height of 0.4 mm, tooth top length of 0.7 mm, tooth base length of 1.0 mm, and tooth base spacing of 0.3 mm. A validation experiment conducted using this optimized configuration demonstrated good forming accuracy in the support contact area, with a deformation value of −0.208 mm, confirming the effectiveness and reliability of the proposed parameters. This study not only provides a theoretical foundation for the optimal design of block supports in LPBF but also offers experimental data and practical guidance for selecting support parameters in the fabrication of overhanging structures. Full article

Additive manufacturing by Selective Laser Melting (SLM) or, precisely, Laser Powder Bed Fusion (L-PBF), offers new opportunities for producing electrically functional metal components with tailored geometric designs and material properties. In this study, the electrical contact resistance and related properties of 3D-printed samples made from Al5086 aluminum alloy are tested. The benefits of Al5086 include flexibility without cracking, welding ability and exceptional resistance to corrosion in saltwater and industrial environments. This makes it an excellent candidate for power electric applications due to its good electrical conductivity and corrosion resistance. In this study, an analysis is performed to assess the impact of internal volumetric properties and surface parameters on general contact resistance performance. This analysis combines advanced testing procedures and parameter identification of the electric contact resistance model. This study investigates how these parameters affect contact resistance, which is a critical factor in the reliability of electrical devices. Electrical contact resistance was measured using a dedicated test setup that applied consistent pressure and maintained directional alignment. The results show that the printing direction of the samples slightly affects resistance values due to the continuity of current paths along the build direction, likely due to homogenous inter-layer boundaries and mechanical stress distribution. These findings suggest that both print orientation and internal structure must be considered when designing 3D-printed contact elements for electrical applications. Overall, this study demonstrates the feasibility of using L-PBF-fabricated aluminum components in electric applications where both electrical and structural performances are essential. Full article

To address the challenges of printing quality and dimensional accuracy in the fabrication of TC11 titanium alloy thin-walled components via laser powder bed fusion (L-PBF), this study systematically optimized the L-PBF process parameters and investigated the printing limits of thin-walled structures, providing theoretical and practical guidance for high-precision manufacturing. First, single-factor experiments were conducted to examine the effects of laser power, scanning speed, and hatch spacing on relative density. Subsequently, response surface analysis was performed using a Box–Behnken design to establish a predictive model with relative density and surface roughness as the response variables, enabling multi-objective parameter optimization. Based on the optimized parameters, a series of thin-walled structures with varying wall thicknesses were fabricated, the resulting printing defects were analyzed, and a mathematical model correlating wall thickness with limiting printing height was established. The response surface model exhibited excellent statistical significance, with an F-value of 0.9930 and a p-value of less than 0.0001, indicating a highly reliable fit. The coefficient of determination (R²) of the model was 0.9889, while the adjusted R² and predicted R² were 0.9747 and 0.9146, respectively, confirming the model’s good predictive capability. The optimal process parameters obtained through the model were a laser power of 190 W, a scanning speed of 1100 mm/s, and a hatch spacing of 0.10 mm. Validation experiments conducted under these conditions yielded a deviation of only 5.33% between the predicted and experimental comprehensive scores, demonstrating the accuracy of the model. A key achievement of this study is the establishment of a piecewise mathematical model relating wall thickness to limiting printing height: a cubic polynomial for wall thicknesses in the range of 0.2 ≤ t ≤ 0.5 mm ($h=107.5t^3-161.5t^2+106.7t-5.86$) and a quadratic polynomial for wall thicknesses in the range of 0.5 ≤ t ≤ 0.8 mm ($h=- 0.25t^2+34.89t+3.17$). This model enables accurate prediction of the formability of thin-walled structures. Full article

Laser powder bed fusion (LPBF) technology has now reached a significant level of commercial maturity, offering some of the most reliable solutions in the additive manufacturing (AM) field. However, AM processes may introduce defects that result in high variability of mechanical properties and low reproducibility. This entails the need to thoroughly understand the behavior of the materials used, studying their response to the different types of stresses typical of real-world applications. The research activity presented consists of the analysis of the mechanical properties of the aluminum alloy AlSi10Mg, which is widely used due to its good strength-to-density ratio. Focus is put on the response to axial, torsional, and combined axial-torsional static and fatigue strength, comparing as-built T6 heat-treated conditions. Full article

Laser Powder Bed Fusion (LPBF) enables the fabrication of complex titanium alloy components with high geometric freedom; however, surface integrity and tribological performance remain critical limitations for sliding-contact applications in biomedical and aerospace systems. In this study, the influence of in-layer laser remelting on the microstructure, surface topography, and dry sliding tribological behavior of LPBF-fabricated Ti-6Al-4V ELI (Grade 23) is systematically investigated. Disc-shaped specimens were produced using single-scan (SS) and double-scan (DS, in-layer remelting) strategies and tested in ball-on-disc configuration against AISI 52100 steel at a constant normal load of 10 N and three sliding speeds of 0.10, 0.15, and 0.20 m·s⁻¹. Microstructural and phase-related characteristics were analyzed by X-ray diffraction combined with Rietveld refinement and Warren–Averbach analysis, revealing that the DS strategy increases retained β-phase fraction (up to 5.2%) and promotes crystallite coarsening relative to the SS condition, without significantly altering bulk hardness. Surface morphology examined by SEM/EDS and AFM revealed a more homogeneous near-surface topography in the DS condition. Tribological results indicate that sliding speed governs steady-state friction and wear, with specific wear rates increasing progressively from 5.13 to 5.44 × 10⁻⁴ mm³·N⁻¹·m⁻¹ for SS and from 6.47 to 7.52 × 10⁻⁴ mm³·N⁻¹·m⁻¹ for DS across the investigated speed range. The DS specimens exhibited higher wear rates than the SS condition across all tested speeds, while steady-state COF values remained comparable between strategies, indicating that remelting-induced microstructural modifications affect material removal mechanisms without proportionally destabilizing the frictional regime. These findings suggest that in-layer laser remelting represents a process-integrated parameter with measurable consequences for surface integrity and tribological performance, though the generalizability of these results warrants validation across broader experimental conditions. Full article

Laser powder bed fusion (LPBF) is susceptible to defects arising from melt pool instabilities, spatter, heat accumulation, and powder spreading anomalies. In situ infrared (IR) monitoring can detect these issues; however, it typically generates large volumes of data that are costly to store and analyze. This work proposes a projection-based framework that directly maps in situ thermal measurements onto a three-dimensional (3D) voxelized part geometry, substantially reducing storage requirements while preserving spatial fidelity. In addition, several IR derived features are incorporated into a practical workflow for defect detection and process model calibration, including laser scan order, local pre-deposition temperature, maximum pre-scan temperature, and spatter generation and landing locations. For completeness, commonly used metrics such as interpass temperature, heat intensity, cooling rate, and relative melt pool area are extracted within the same unified processing pipeline. All features are computed using a consistent, reproducible Python-based implementation to streamline integration into routine monitoring and analysis tasks. Multiple parts are fabricated, monitored, and characterized to evaluate the proposed framework, demonstrating that the extracted features reliably identify process anomalies and correlate with observed defects. Full article

This study elucidates the impact of laser volumetric energy density (VED) on the densification behavior, microstructural evolution, wear resistance, and corrosion resistance of high-Nb TiAl alloys fabricated via laser powder bed fusion (LPBF). Experimental characterization results showed that relative density first increased and then decreased with increasing VED, reaching a maximum density of 97.13% at 66.67 J/mm³. Across the process windows, the high-Nb TiAl alloys were primarily composed of γ-TiAl, α2-Ti3Al, and β/B2 phases with varied proportions. Mechanical property analysis showed that the alloy attained a maximum average hardness of 422 HV_0.5 at 81.48 J/mm³, due to the accumulation of harder α2 and B2 phases. However, the high-Nb TiAl alloys fabricated at 66.67 J/mm³ exhibited excellent wear resistance, as evidenced by wear track widths and depths of 971.71 μm and 21.83 μm, respectively. Abrasive and oxidative wear were identified as the primary mechanisms. Meanwhile, this specimen also exhibited excellent corrosion resistance, a corrosion current density of 1.421 × 10⁻⁶ A/cm², attributed to the coupled dense passive film of TiO₂ and Al₂O₃ that prevented chloride ingress. The findings in this work may provide a critical experimental reference and theoretical underpinnings for LPBF-fabricated lightweight structural materials. Full article

Laser Powder Bed Fusion for metals (PBF-LB/M) is a complex additive manufacturing process in which metal powder is selectively melted layer-by-layer to fabricate 3D parts. Process parameters critically influence the resulting microstructure in nickel alloys, with features such as melt pool marks, grain size and orientation, porosity, and cracks serving as key process signatures. These features are typically analyzed post-process to identify suboptimal conditions. This research aims to develop automated post-process measurement and analysis techniques using image processing, pattern recognition, and statistical learning to correlate process parameters with part quality. Optical microscopy images of build surfaces are analyzed using machine learning algorithms to evaluate porosity, grain size, and relative density in fabricated test coupons. Effect plots are generated to identify trends related to increasing energy density. A novel deep learning approach based on Mask R-CNN is used to detect and segment melt pool regions in optical microscopy images. From the segmented regions, melt pool dimensions—such as width, depth, and area—are extracted using bounding geometry coordinates. Manually labeled images (Type I and Type II) are used to train the model. A comparison between ResNet-50 and ResNet-101 backbones shows that the ResNet-50-based model (Model 2) achieves superior performance, with lower training loss (0.1781 vs. 0.1907) and validation loss (8.6140 vs. 9.4228). Quantitative evaluation using the Jaccard index, precision, and recall metrics shows that the ResNet-101 backbone outperforms ResNet-50, achieving about 4% higher mean Intersection-over-Union, with values of 0.85 for Type I and 0.82 for Type II melt pools, where Type I is detected more accurately due to its more regular morphology and clearer boundaries. By extending Faster R-CNNs with a mask prediction branch, the method allows for precise melt pool measurements, providing valuable insights into process quality and dimensional accuracy, and aiding in the detection of defects in PBF-LB-fabricated parts. Full article

This paper investigates how the thickness of dogbone tensile specimens made from heat-treated Hastelloy-X alloy produced by Laser Powder Bed Fusion (LPBF) influences their mechanical properties and microstructure. The focus of the investigation is on surfaces in an “as-built” condition and considers a range of thickness from 3 to 1 mm. The “as-built” surfaces condition is a fundamental outcome, considering that LPBF technology’s key feature is the ability to produce intricate and complex geometries that are difficult to achieve with conventional manufacturing technologies. The specimens were fabricated according to ASTM E8/E8M-21 and were heat-treated in a vacuum furnace at 1150 °C for two hours. The microstructure of the material was evaluated through porosity, EBSD, and Microhardness analyses. The mechanical properties were evaluated through tensile tests conducted at room temperature on dogbone specimens fabricated both parallel and perpendicular to the building direction. The findings indicate a significant reduction in mechanical properties that could be correlated with the reduction in specimen thickness, reflecting a gradual decline from the baseline. Specifically, a 14% decrease in Ultimate Tensile Strength (from 612 to 528 MPa), an approximately 19% reduction in Young’s Modulus (from 190 GPa to 153 GPa), and a 32% decrease in Elongation at Break (from 59.2% to 40.0%) were observed. Furthermore, it was noted that the printing orientation of the specimens significantly affects their mechanical properties, regardless of thickness. Overall, the results suggest that applying standard heat treatment under specific conditions, such as with a thin, exposed wall of about 1mm with a striped strategy, may not lead to adequate material performance. Full article

FeCrMnNi-based alloys, derived from the well-known Cantor high-entropy alloy, have attracted increasing attention due to their excellent strength–ductility balance. Additively manufactured FeCrMnNi variants are characterized by superior hardness compared to their conventionally processed counterparts. In the present study an optimized composition of the FeCrMnNi medium-entropy alloy was additively manufactured via laser-based powder bed fusion and subsequently subjected to systematic heat treatments. CALPHAD simulations were applied to select the specific composition and post-processing heat treatment conditions, where the latter aimed at promoting the evolution of a dual-phase microstructure. Experimental characterization included X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and electron backscatter diffraction, as well as Vickers hardness and tensile testing. A microstructure could be established dominated by a face-centered cubic (FCC) phase with minor fractions of a secondary phase in the non-treated condition. The evolution of an additional body-centered cubic (BCC) phase upon heat treatment at and above 700 °C was observed. The emerging BCC phase as well as increasing fractions of the secondary phase were accompanied by significantly increased hardness and strength, surpassing the literature values of similar compositions. However, a heat treatment at 1000 °C resulted in recrystallization and an increase in grain size, while the decreasing fraction of the secondary phase eventually led to a reduction in strength. These findings underscore the combined potential of composition optimization and targeted post-processing to enhance the mechanical performance of additively manufactured FeCrMnNi alloys. Full article

The transition to a circular economy requires assessment tools that capture not only the environmental and economic performance of products but also their circular design, functionality, and durability. While Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) are widely used, they alone do not capture modularity, reparability, reuse potential, or product lifespan. This study introduces a novel, integrated framework combining LCA, LCC, and product-level circularity indicators to provide a holistic evaluation of sustainability and circularity. In this study, two types of injection molds for plastic part production are compared: a conventionally manufactured mold and an additively manufactured metal mold produced by Laser Powder Bed Fusion (L-PBF) technology. The comparison integrates Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and a set of micro-circularity indicators, including the Material Circularity Indicator (MCI), Recycling Desirability Index (RDI), circular design guidelines (CDG), Disassembly Effort Index (DEI), longevity indicator (LI), and Circular Economy Indicator Prototype (CEIP). Results show that the AM mold exhibits lower environmental impacts across almost all categories, while its slightly higher initial cost is largely offset by reduced indirect costs over the product lifecycle. Micro-circularity indicators reveal that the AM mold achieves higher material circularity and better circular design performance (MCI, CDG, CEIP) but shows only minor improvements in disassembly and recyclability (DEI, RDI) and lower longevity (LI) compared to the conventional mold, highlighting potential limitations for remanufacturing and end-of-life recovery. The novelty of this study lies in the integrated application of LCA, LCC, and multiple micro-circularity indicators, providing an operational framework for evaluating circular design, reparability, and durability in additive manufacturing and enabling informed, holistic decision-making for truly circular products. Full article