Search Results (2,644)

As semiconductor packaging technology evolves from two-dimensional to three-dimensional integration, the through-glass via (TGV) technique, as a core interconnect method in advanced packaging, is emerging as a strong candidate to replace through-silicon vias (TSVs) and plated through-holes (PTHs) in organic substrates. Glass substrates offer excellent electrical insulation, low dielectric loss, tunable thermal expansion coefficients, and the potential for large-scale panel-level manufacturing. However, issues related to TGV hole quality, metallization uniformity, and thermomechanical reliability remain key bottlenecks limiting their large-scale industrialization. This investigation provides a comparative review of non-laser and laser machining for TGVs to address the above problems. First, the technical background and core advantages of TGVs are outlined. Second, this study details non-laser processing methods, including sandblasting erosion, mechanical drilling, the photosensitive glass method, electrochemical discharge machining (ECDM), deep reactive ion etching (DRIE), and others. Third, laser processing methods, covering laser ablation drilling, laser-induced deep etching (LIDE), femtosecond laser-assisted wet etching and others, are given focus. Moreover, this study analyzes typical applications of TGVs in 3D/2.5D packaging, MEMS devices, optoelectronic integration, and others. In addition, the machining processes of non-laser and laser-based TGVs, such as mechanical machining, ECDM, and LIDE, are compared, and key process challenges, technical trade-offs, and reliability failure mechanisms are discussed. Finally, this review looks ahead to future trends, aiming to provide a systematic technical reference for researchers in the TGV field. Full article

Metal matrix composites (MMCs) are engineered multiphase materials in which a continuous metallic matrix contains deliberately introduced reinforcing phases. Their properties arise from the combined effects of the matrix, reinforcement, interface, processing route and spatial architecture. The matrix provides metallic continuity, plastic deformation capacity, processability and thermal or electrical conduction, whereas the reinforcement is selected to modify stiffness, strength, hardness, wear resistance, thermal stability, corrosion response or functional behavior. From a practical interpretation standpoint, MMC performance should not be ascribed solely to the reinforcement fraction, but rather to the coupled effects of reinforcement distribution, interfacial bonding, porosity, residual stress, heat-treatment state, and architecture. Full article

Laser powder bed fusion (LPBF) is a widely utilized metal additive manufacturing (AM) process for fabricating intricate geometries with high mechanical strength. However, achieving defect-free parts remains challenging due to complex thermodynamics and process variability. Component quality is primarily determined by mel-pool morphology, which depends on key process parameters such as laser power, scan speed, and layer thickness. Improper parameter selection causes defects like porosity (keyhole and lack of fusion), balling, and residual stresses, compromising structural integrity. Optimizing these parameters is crucial but difficult due to the multi-scale, multi-physics nature of the process, which traditionally relies on costly, time-intensive experimental trials. We present results from a data-driven approach using machine learning (ML) models to predict and optimize LPBF melt-pool characteristics, reducing reliance on trial-and-error experimentation. We find that laser power and scan speed predominantly influence the melt-pool formation. Higher scan speeds produce more favorable melt pools, whereas excessive laser power at low scan speeds leads to deep keyhole defects. To predict and classify melt pools efficiently, several ML models are deployed, including logistic regression, decision trees, ensemble learning, and fully connected neural networks. The standard neural network achieved the highest cross-validated macro-F1 score of 0.978 ± 0.014, while the weighted neural network achieved the highest recall for the rare optimal melt-pool class, 0.967 ± 0.050. These findings show that class-weighted learning provides a recall-oriented strategy for identifying suitable LPBF process windows, while avoiding overreliance on single train-test split performance. The findings underscore the effectiveness of ML in accurately classifying LPBF melt pools to rapidly identify optimal process parameters. Full article

Friction stir welding (FSW) began as a fairly specialized joining method, but over the past three decades it has evolved into something considerably more versatile, a manufacturing platform that now handles complex multi-material assemblies and solid-state additive processes with reasonable reliability. This review follows this evolution, paying particular attention to friction stir additive manufacturing (FSAM) and the persistent difficulties that arise when joining dissimilar systems, such as aluminum to steel or metals to polymers, where the fate of the joint is largely decided by how well the intermetallic compounds are kept under control. Machine learning, artificial intelligence, and high-fidelity numerical models are reducing the reliance on trial-and-error that once dominated parameter selection and defect prediction, bringing FSW closer to the operating principles of Industry 4.0. Hybrid variants, including ultrasonically assisted and underwater FSW, also receive attention here, as they offer researchers finer control over heat generation and plastic flow than the standard process allows. Throughout the study, microstructural observations are directly connected to mechanical results, with the aim of analyzing the current state of solid-state manufacturing and identifying the questions that most urgently need answering. Full article

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article

Micro-structured SAE H13 tool steel inserts for polymer injection molding require accurate replication of sub-millimeter features while retaining adequate densification and heat-treatment response. This study compared two powder-based routes on the same hemispherical insert containing pyramidal features of approximately 0.145 mm base width: hot embossing (HE) of water-atomized SAE H13 powder (supplier d50 = 5.7 µm, irregular morphology) compounded with a commercial M1 binder, and material extrusion (MEX) of a commercial gas-atomized SAE H13 filament processed on a Markforged Metal X. Rheological screening selected a 57:43 vol% powder-to-binder ratio for the in-house HE feedstock, and DSC/TGA measurements defined two-step debinding windows. The best HE conditions were 220 °C, 8 MPa, and 45 min for the in-house mixture, and 210 °C, 8 MPa, and 30 min for the granulated commercial filament; the latter showed a 0.15% linear deviation from the silicone replica diameter among the best-rated samples. Under the tested commercial MEX configuration, the pyramidal features were not resolved because the 0.40 mm deposition line width exceeded the target feature base width, causing the slicer to omit the sub-line-width geometry. The defect populations differed qualitatively: HE specimens showed porosity and local cracking associated with powder morphology and pressureless sintering, whereas MEX specimens showed build-direction-aligned inter-raster voids associated with the toolpath. Microhardness and tensile data are therefore interpreted as process-history-specific results rather than as a direct route ranking, because sintering conditions were not uniform across all specimens. The study defines an experimentally bound process-selection limit for SAE H13 micro-tooling: HE remains preferable for sub-nozzle surface features, whereas MEX remains attractive for macro-scale geometric freedom, if resolution, densification, and post-sintering consolidation are addressed. Full article

Additive manufacturing has emerged as a transformative technology for fabricating complex drug-eluting medical devices, offering unprecedented design freedom and functional integration capabilities. This comprehensive review systematically analyzes 3D printing technologies applied to pharmaceutical device manufacturing, focusing on drug-eluting vascular stents as a representative application. This review covers six primary additive manufacturing techniques, ranging from high-resolution vat photopolymerization (25 μm resolution) to direct energy deposition, with a focus on their capabilities for produce pharmaceutical devices with controlled drug release properties. Novel 4D/5D/6D printing technologies introduce stimuli-responsive behaviors enabling programmable drug release profiles and adaptive device functionality. Manufacturing process optimization reveals superior design flexibility compared to conventional methods, with 85–95% reduction in design iteration time and elimination of tooling costs for complex geometries. The material landscape encompasses traditional metals (316L stainless steel, cobalt–chromium), biodegradable polymers (polylactic acid, PLA; polycaprolactone, PCL; poly(lactic-co-glycolic acid), PLGA), shape-memory materials (i.e., polymers and alloys capable of recovering a pre-programmed shape upon exposure to a specific stimulus such as body temperature, moisture, or light), and advanced nanocomposites, each offering distinct drug-loading capacities (100–500 μg/cm²) and release kinetics. Critical challenges include standardization requirements (International Organization for Standardization (ISO) 5840 and American Society for Testing and Materials (ASTM) F2606), pharmaceutical-grade manufacturing protocols, and regulatory pathways for novel drug-device combinations. This review identifies key research priorities including development of biocompatible printing materials, accelerated drug release testing protocols, and scalable manufacturing processes suitable for medical device production. This analysis demonstrates that 3D printing enables integration of multiple pharmaceutical functions within single devices, controlled spatiotemporal drug delivery, and elimination of secondary manufacturing steps for drug coating processes, advancing the development of next-generation therapeutic medical devices. Full article

Wire-arc directed energy deposition (arc-DED) of maraging 250 (M250) steel is of growing interest for aerospace, tooling, and defense applications, yet systematic process characterization data remain limited. This study presents a mixed quantitative–qualitative factorial comparison of three Fronius synergic transfer modes, GMAW-CMT-Mix, GMAW-CMT-Universal, and GMAW-Pulsed-Arc, for single-bead M250 deposition across wire feed speeds of 4.45 to 8.26 m/min and travel speeds of 0.3 to 1.5 m/min. Bead geometry and process behavior are characterized using non-contact optical profilometry and destructive methods (i.e., metallographic sectioning, optical microscopy, and Vickers microhardness). The material feed rate ratio, R_wt, is introduced as a unifying process descriptor; heat input and cross-sectional area scale linearly with R_wt, while travel speed primarily governs bead height and wire feed speed primarily governs bead width. At the highest travel speed tested, GMAW-CMT-Mix and GMAW-Pulsed-Arc exhibit bead humping, rendering those conditions unsuitable, while GMAW-CMT-Universal maintains stable deposition with consistent dilution and the lowest heat input at equivalent R_wt. GMAW-CMT-Mix yielded the highest dilution and hardness. Linear regression of process responses against R_wt gives $R^2$ exceeding 0.83 for both height and width across all modes. These results establish a characterization baseline supporting future multi-layer studies. Full article

Green synthesis of metal nanoparticles has attracted significant attention due to its sustainability, yet achieving precise control over their physicochemical properties via continuous-flow systems remains a challenge. This study evaluates the sustainable synthesis of copper nanoparticles using 3D-printed microfluidic reactors fabricated via the fused filament technique with glycol-modified polyethylene terephthalate. A systematic experimental design was performed to investigate the effects of the reducing agent concentration, the channel architecture, and the medium pH on particle size and morphology. Fluid dynamics theoretical modeling revealed a laminar flow regime, dominated by advection, where the serpentine geometry successfully induced stable homogeneous mixing. Statistical analysis identified pH as the most critical factor, demonstrating that an alkaline medium of pH 8 combined with a 5:1 reductant-to-precursor ratio optimizes the production of uniformly spherical copper nanoparticles with significantly smaller diameters. Advanced experiments also assessed the influence of flow rates and stabilizer agents on particle size, morphology and purity. These findings validate the integration of additive manufacturing and continuous microfluidics as a robust, low-cost, and eco-friendly platform for the reproducible and scalable production of metallic nanoparticles. Full article

Additive–subtractive hybrid manufacturing (ASHM) allows for the rapid manufacturing of metal components with complex and precise geometries for ready-to-use or near-ready-to-use applications. Laser wire-directed energy deposition (LW-DED) can be used to quickly manufacture metal components, while CNC machining can achieve precise geometric tolerances. In this study, Ti-6Al-4V alloy specimens were fabricated using an LW-DED process combined with CNC machining and tested to evaluate the effects of ASHM on mechanical performance. Post fabrication, the Ti-6Al-4V material was evaluated through hardness mapping, monotonic tensile testing, and fully reversed axial fatigue testing. Vicker’s micro-hardness mapping showed a range of hardness results from 300 to 350 HV in the ASHM Ti-6Al-4V that remained consistent throughout the build. Tensile results showed a similar response to cast and wrought Ti-6Al-4V, with an average yield stress of 819.4 MPa, ultimate tensile strength of 935.5 MPa, and modulus of 119 GPa. When tested in fatigue, the material had a reduced life compared to wrought Ti-6Al-4V, which is attributed to defects originating from the additive process. While no run-outs were observed from the testing, the fatigue results remain aligned with trends reported for other methods of additively manufactured Ti-6Al-4V. Fully reversed high-cycle fatigue loading revealed that the ASHM-fabricated Ti-6Al-4V fell into a Basquin power-law fit with a fatigue strength coefficient of 1942 MPa with a fatigue strength exponent of −0.115. The fatigue life of the ASHM material is found to be dependent on the resulting porosity of the material that stems from the LW-DED process used in the ASHM process described. Full article

Additive manufacturing (AM) of metallic lattice structures, particularly those made of Ti-6Al-4V, has significant potential for biomedical applications due to their lightweight nature and favorable mechanical properties. However, laser powder bed fusion (LPBF) processes often introduce residual stresses and distortions, which compromise dimensional accuracy and part performance. This study presents a simulation-based approach for optimizing process parameters and post-processing strategies to minimize these issues. Using the Simufact Additive 1.0 ink, 2023 software, voxel sensitivity analysis was conducted to identify an optimal mesh size of 0.35 mm. A Design of Experiments (DoE) approach in MINITAB was applied to optimize key LPBF parameters, including laser power, scanning speed, and scan width. Simulations incorporating stress relief and hot isostatic pressing (HIP) were conducted to assess their impact on residual stresses and distortions. The results show that stress relief effectively reduces maximum distortion by up to 50% in the X direction and 17% in the Z direction for an FCC lattice structure with a 0.75 mm strut thickness. HIP further decreases deflection angles by 77%. The simulation predictions correlate well with the experimental measurements, supporting the use of simulation-driven process optimization to enhance the dimensional stability and mechanical reliability of Ti-6Al-4V lattice structures for biomedical implants. Full article

This study investigates the application of advanced metal additive manufacturing (AM) and topology optimization for the development of a structurally efficient and lightweight 3U nanosatellite frame. Payload weight is a critical factor in space mission costs; therefore, a stock 3U CubeSat design was subjected to structural optimization using specialized Generative Design Software. The optimized model was fabricated using Powder Bed Fusion—Direct Metal Laser Sintering (PBF-DMLS) on an EOS M290 metal printer with AlSi10Mg aluminum alloy. While AlSi10Mg differs in ultimate tensile strength from traditional wrought aerospace alloys, it was selected to evaluate the baseline feasibility of this application. To evaluate manufacturability and preliminary performance, Finite Element Analysis (FEA), including structural and modal response analyses, was conducted. While the optimized design successfully achieved a 53% mass reduction (from 333 g to 155 g) and met the 30 Hz minimum fundamental frequency requirement, static analysis indicated a maximum simulated stress of 287 MPa. Because this exceeds the material’s nominal yield strength of 220 MPa, localized plastic deformation is predicted in the bare-frame configuration under maximum launch loads. This necessitates further design iterations and full-assembly simulations, incorporating the load-sharing effects of integrated panels prior to physical qualification. Post-processing successfully met JAXA dimensional and surface roughness requirements. Ultimately, this study serves as a foundational manufacturability baseline, demonstrating the applicability of PBF-DMLS for nanosatellites. Full article

Additive manufacturing (AM) enables the fabrication of complex, customisable components from metals, composites and polymers such as polylactic acid (PLA); however, the process commonly produces poor surface finishes and inherent defects. Centrifugal disc finishing (CDF) is an established mass finishing technique in conventional manufacturing but remains insufficiently characterised for additively manufactured polymers. This exploratory study investigates the influence of CDF on fused deposition modelling (FDM)-fabricated PLA components with varying geometrical features, focusing on three-dimensional surface parameters including average areal surface roughness, skewness and kurtosis. Samples were processed up to 720 min with analysis at predetermined intervals to capture transient and steady-state-like behaviour. Surface characterisation was conducted using non-contact optical interferometry to obtain quantitative roughness data and three-dimensional topographical maps, supported by digital optical microscopy and gravimetric analysis to quantify material removal rates. Analysis of the experimental data indicated apparent relationships between processing time, geometry and surface response. Results indicate that material removal behaviour and roughness evolution may be geometry-dependent. Flat and convex surfaces appeared to follow expected transient-like and steady-state-like behaviour, whereas restricted geometries and intricate features exhibited distinct responses with characteristic transition times. Surface roughness reductions ranged from 36% to 89% depending on geometry. These findings provide preliminary quantitative insight into geometry-specific mass finishing behaviour, supporting improved process understanding and informing future optimisation of post-processing strategies for additively manufactured polymer components. Full article

The increasing demand for sustainable and lightweight furniture systems has driven interest in additively manufactured polymer components as alternatives to conventional metal hardware. However, their performance at the functional assembly level under standardized loading conditions remains insufficiently explored. This study evaluates the feasibility of replacing metal drawer slides with fused deposition modeling (FDM)-based polymer alternatives fabricated from polylactic acid (PLA) and polyethylene terephthalate glycol (PETG). Unlike previous studies focused on material-level characterization, this work investigates fully functional drawer slide assemblies integrated into medium-density fiberboard (MDF) systems, enabling component-level assessment under realistic conditions. Specimens were designed in SolidWorks and fabricated under controlled printing parameters. Commercial metal slides were used as benchmarks. Mechanical performance was tested according to BS EN standards, and deformation was measured at multiple points. Statistical analysis included ANOVA, Tukey HSD, and t-tests at a 95% confidence level. Results showed significant differences among materials (p < 0.05). Metal slides exhibited the highest stiffness and minimal deformation. PLA showed stable performance with minor surface degradation, while PETG demonstrated lower dimensional stability and premature failure due to higher compliance. Overall, PLA-based FDM components offer a cost-effective alternative for non-heavy-duty applications, whereas PETG requires further optimization. The study bridges additive manufacturing and real-world furniture component performance under standardized testing. Full article

Accurate detection and reliable grasping of small bolts are essential for intelligent manufacturing and automated assembly. However, this remains a challenge due to the small size, slender geometry, and metallic reflective surfaces of bolts. In this paper, we propose a vision-guided robotic bolt handling framework that integrates lightweight object detection, optimization-oriented grasp execution, and collision-aware trajectory planning. The lightweight YOLOv8n-BoltLite detector, improved with E-C2f, LCA, SA-PAN, and WD-IoU loss, enhances localization accuracy and feature representation for small and slender bolts. A robotic grasping framework is designed to transform detection results into executable robotic actions through 3D pose estimation, mid-shank grasp point generation, and optimization-oriented execution formulation. Additionally, a five-segment trajectory planning strategy ensures safe and efficient robot motion. Experimental results show that YOLOv8n-BoltLite achieves a five-run average mAP of 99.64 ± 0.05% with 198 FPS, and 3.02 M parameters. On an additional challenging external test set involving illumination variation, clutter, partial occlusion, reflection, and clustered bolts, the proposed detector achieves 94.62 ± 0.18%, outperforming recent lightweight detectors under the same training protocol. Robotic experiments involving 1000 controlled grasping trials and 300 multi-target grasping attempts demonstrate a controlled-condition success rate of 97.0% and improved target-selection reliability in multi-bolt scenes. These results suggest that the proposed framework offers a practical and efficient solution for automated bolt handling in intelligent manufacturing environments. Full article