Search Results (2,613)

Understanding the finishing behavior of hybrid structures produced by additive manufacturing based on FDM is critically important in systems where phases with different thermal and mechanical properties coexist. In this study, the drilling performance of hybrid structures with a PLA/17-4 PH/PLA layer arrangement was comprehensively investigated in terms of thrust force, moment, surface roughness, temperature variation, vibration behavior, and surface integrity. For this purpose, a total of 16 drilling tests were performed on 56 × 56 × 15 mm hybrid specimens with 100% infill density, in a full factorial configuration, at cutting speeds (V) of 80–170 m/min and feed rates (f) of 0.04–0.16 mm/rev. The middle layer was used in the as-printed green state as a 17-4 PH metal-filled filament containing metal particles and binder, without any debinding or sintering step. The results showed that increasing feed rate increased thrust force, moment, and surface roughness in all layers, whereas increasing cutting speed decreased these values and promoted a more stable drilling regime. The middle 17-4 PH layer exhibited lower surface roughness than the outer PLA layers, while thermal measurements indicated limited variation at the hole entrance and higher temperature accumulation at the hole exit. The most favorable drilling condition within the studied hybrid configuration was obtained at 170 m/min and 0.04 mm/rev, whereas the least favorable condition was obtained at 80 m/min and 0.16 mm/rev. Overall, the combination of high cutting speed and low feed rate provided the most suitable drilling window for the studied hybrid structure. The findings also indicated that surface quality was more strongly associated with cutting load and high-frequency vibration components than with vibration level alone. Full article

This study presents a hybrid additive manufacturing approach to fabricate bioinspired stainless steel 316L-copper (SS316L-Cu) multimaterial structures using laser powder bed fusion (LPBF). The present study incorporates honeycomb lattice structures with varying wall thicknesses (0.25 mm, 0.5 mm, 0.75 mm, and 1.0 mm) to investigate the effect of geometric parameters on mechanical performance. Mechanical testing was conducted according to ISO 6892 standards, and the results revealed a strong dependence of tensile strength and ductility on lattice thickness. Copper (Cu) infiltration into SS316L lattice structures improved ductility by 30% compared to the monolithic SS316L lattice, with minimal compromise in tensile strength. To complement experimental results, molecular dynamics (MD) simulations were performed to study atomic-scale deformation and validate the trend of strength enhancement with increasing wall thickness. The findings demonstrate the potential of combining LPBF and liquid Cu infiltration to develop multifunctional, mechanically robust, and thermally conductive metallic composites. This approach provides valuable insight into structure–property relationships and supports the design of next-generation multifunctional composites for structural and thermal applications. Full article

Wire arc additive manufacturing (WAAM) has emerged as a promising technology for producing large-scale metal components due to its high deposition efficiency, low material cost, and design flexibility. However, the widespread industrial adoption of WAAM is hindered by challenges in geometric accuracy, process stability, and defect control, which are closely related to two critical aspects: trajectory planning and real-time weld seam tracking. This review provides a comprehensive and critical analysis of recent advances in both fields, with an emphasis on their interconnection rather than treating them as separate research streams. Unlike existing reviews that primarily summarize path planning algorithms or image processing techniques in isolation, this paper explicitly examines the integration challenges and synergistic potential between offline trajectory optimization and online vision-based monitoring. Key topics include adaptive path strategies for sharp corners and intersections, interlayer filling methods to mitigate heat accumulation and residual stress, as well as passive and active visual sensing technologies for molten pool characterization and defect detection. The review further identifies a persistent gap in closed-loop systems that combine real-time image feedback with dynamic path replanning. Based on the analysis of representative studies, current limitations are discussed and future research directions are proposed, including the development of digital twins, multi-modal data fusion, and reinforcement learning-based adaptive control. This review offers a distinct perspective aimed at advancing intelligent, high-precision WAAM systems for complex metal components. Full article

The development of additive manufacturing in steel construction opens new possibilities for shaping structurally efficient and geometrically differentiated load-bearing systems. At the same time, the viability of such solutions depends strongly on their material rationality, especially at the scale of larger structural typologies. This paper presents a computational comparative screening of spatial steel roof typologies that may be relevant for future large-scale metal additive manufacturing, focusing on how global geometry, support arrangement, curvature, and structural depth influence mass efficiency under a unified structural modelling framework. Using computational modelling and comparative evaluation, the study examines how variations in structural form influence the performance of spatial systems developed within a unified design framework. The analysis demonstrates that the potential for material rationalisation of such structures is not limited to local modification of member dimensions, but is fundamentally linked to the configuration of the overall structural geometry. More than 40 structural configurations were analysed, covering seven typological variants, three rise levels, two support strategies, and two section-sizing approaches. An additional threshold sensitivity check was performed for representative variants to examine whether the main typological ranking remained stable under an alternative four-group utilisation classification. The obtained masses varied by more than one order of magnitude between the most and least favourable configurations, confirming the strong influence of global typology and support arrangement on material demand. The results highlight the importance of structural typology, support arrangement, and geometric organisation in achieving material-efficient solutions. The study therefore argues that, in the context of steel structures considered for future additive manufacturing, global form should be treated as a primary design variable rather than as a secondary outcome of local member sizing. Full article

Laser-induced breakdown spectroscopy (LIBS) offers rapid, in situ, and multi-element detection, and therefore shows strong potential for quality monitoring of metal powders in additive manufacturing. However, direct LIBS analysis of flowing metal powders is often affected by particle splashing, unstable laser–particle coupling, and plasma fluctuations, which reduce signal repeatability and detection reliability. To address these issues, this study developed an integrated measurement and classification framework for identifying cross-contamination in additive-manufacturing metal powders. A stable powder particle stream was generated through vibratory feeding and particle-flow focusing, while a hollow quartz tube with a side opening was introduced to provide cylindrical spatial confinement, thereby improving the stability of laser–particle interaction and enabling in situ spectral acquisition without pellet preparation. TC4 powder was used as the base material and AlSi10Mg powder as the contaminant, and samples with contamination levels of 0, 0.5, 1, 2, and 5 wt.% were prepared. Two independent batches of single-shot LIBS spectra were collected. To reduce the influence of strong spectral fluctuations, outlier spectra were removed using full-spectrum total-intensity quantile filtering, followed by asymmetric least-squares baseline correction and standard normal variate transformation. PCA combined with multiple machine-learning models was then applied for contamination identification. The results showed that LIBS spectra at different contamination levels exhibited distinguishable distributions in principal-component space, and the spectral differences between clean and contaminated powders became more pronounced with increasing contamination level. In binary classification, several models achieved high classification accuracy at medium and high contamination levels, while PCA-SVM-RBF showed the best performance at low concentrations. In five-class cross-validation, the 5 wt.% class exhibited the clearest decision boundary, whereas confusion remained among low and adjacent contamination levels, indicating that contamination-induced spectral responses followed a more continuous transition. These results demonstrate that the proposed spatially confined particle-flow LIBS framework combined with machine-learning classification can effectively achieve rapid identification of cross-contamination in additive-manufacturing metal powders and provides a feasible technical route for online powder quality monitoring. Full article

A blockade or severe disruption in the Strait of Hormuz would test energy supply resilience by reducing crude oil and LNG availability and by raising routing, freight, insurance, port-handling, warehousing, and transport-support costs. This paper develops a short-run multi-regional input–output stress test to assess where such an energy-route shock enters the production system, how reserves and inventories reduce pass-through, which cross-border links carry residual costs, and where final demand absorbs them. Using the OECD ICIO 2025 edition for 2022, we map the shock to oil and gas extraction, refining, utilities, transport, and transport-support sectors, with an additional premium for major Gulf energy exporters. We propagate the shock for seven input–output rounds under inventory damping. First-round exposure and later-round burden do not coincide, as energy-intensive materials, aviation services, chemicals, minerals, metals, electronics, and machinery face higher downstream costs through material and logistics purchases. With 30% inventory absorption, the upstream energy shock needed for downstream manufacturing to reach a 10% added-cost threshold rises from 73.6% to 85.3%. The results support targeted reserve release, coordinated rerouting, port-logistics priority, inventory management around high-value links, and continuity protection for vulnerable sectors. Full article

Structural health monitoring (SHM) of additively manufactured (AM) small and complex components is investigated using a sensor-based signal processing and machine-learning framework. Guided-wave responses acquired from piezoelectric transducers are analyzed to evaluate the performance of sweep-sine and pulse excitation signals, as well as the influence of infill patterns, part geometry, and defect type on system reliability. Test specimens, including dogbone structures and a simulated rocket-nozzle component, were fabricated using AM, and nonstationary guided-wave signals were recorded and processed. Time–frequency signal representations (scalograms) were generated using the Continuous Wavelet Transform (CWT). Convolutional Neural Networks (CNNs) and Gaussian Mixture Models (GMMs) were employed for supervised classification and unsupervised clustering, respectively. Sweep-sine excitation consistently yielded higher classification accuracy, with CNN analysis achieving near-perfect performance and GMM clustering demonstrating improved group separability. In contrast, pulse excitation revealed transient signal features associated with wave interactions, including reflections, mode conversion, and scattering, highlighting its potential for complementary signal-based diagnostics. Importantly, the proposed hybrid supervised–unsupervised learning framework enables the quantification of previously unseen intermediate load states, demonstrating strong adaptability and generalizability beyond the conditions represented in the training data. Full article

Thin-film solar cells (TFSCs) remain a cornerstone of the global transition toward renewable energy, characterized by consistent reductions in manufacturing costs and steady gains in power conversion efficiency. In addition to electricity generation, TFSCs play an important role in advanced solar thermal cooling, heating, and energy storage systems, where their tunable optical absorption, low thermal mass, and flexibility enable integration with photovoltaic–thermal (PV/T) collectors, thermally driven cooling cycles, and hybrid thermal–electrical storage architectures. This paper provides a comprehensive review of prominent TFSC technologies, including copper indium gallium selenide (CIGS), cadmium telluride (CdTe/CdS), amorphous silicon (a-Si), copper zinc tin sulfide (CZTS), organic photovoltaics (OPVs), and metal halide perovskite solar cells (PSCs), with a focus on their material structures, performance specifications, and current efficiency benchmarks. Compared to state-of-the-art reviews, this article distinguishes itself by addressing next-generation innovations, cross-domain solar thermal–photovoltaic applications, and economic analysis. Specifically, the integration of machine learning and simulation-based material dynamics is examined to accelerate material discovery, process optimization, and the characterization of novel TFPV components relevant to coupled thermal–electrical energy systems. Furthermore, the study explores how additive manufacturing is transforming the industry through the development of high-efficiency electrodes, electrohydrodynamic atomization for thin-film deposition, and the fabrication of flexible solar arrays suitable for thermally integrated and building-scale energy systems, including space applications. By integrating advancements in module efficiency, scalable manufacturing approaches, and techno-economic analysis, this paper positions TFSCs as sustainable, resource-abundant technologies essential for next-generation solar thermal cooling, heating, and energy storage infrastructures. Full article

Polymer-based composites with magnetic properties are promising materials that are able to combine the usual polymer features (low density, high electrical resistance, enhanced flexibility, and processability, etc.) with magnetic properties typically associated with ferro- or ferrimagnetic metals, alloys or metal oxide. The combination of recycled NdFeB powders with additive manufacturing techniques based on material extrusion enables the production of magnetic composites. The novelty of this approach lies in the use of 3D printing supported by an external magnetic field, which is used to align the particles during the printing process and thus improve the final magnetic properties. This approach represents a sustainable strategy for the recovery of electronic waste, converting it into high-value-added magnetic materials intended for additive manufacturing applications. Micrometric particles made of a Neodymium–Iron–Boron (NdFeB) alloy are compounded with a flexible thermoplastic matrix made of polybutylene adipate-co-terephthalate (PBAT). The NdFeB alloy is recovered from permanent magnets of obsolete hard drives and is demagnetized, ground to powder under an inert atmosphere, and finally sieved to a particle size below 50 µm. The obtained powder is mixed with the polymer using a twin-screw extruder. The composite material containing the NdFeB particles is then processed to obtain a calibrated filament, used for the fused deposition modeling (FDM) three-dimensional (3D) printing of magnetic composites. To improve the composite’s ferromagnetic behavior, the particles were aligned along the stacking direction of the layers during the 3D FDM process by printing directly onto a permanent magnet placed on the build plate. Composites containing up to 50% by weight of recycled NdFeB powder were successfully processed using FDM technology, exhibiting increased stiffness, with the storage modulus rising from 123 to 178 MPa at 20 °C, while magnetic field-assisted printing increased the remanence from 11 to 28 emu/g and improved the reduced remanence from 0.21 to 0.49, corresponding to an estimated fourfold improvement in the magnetic energy product. Full article

There is a growing demand for extreme miniaturization and enhanced sensitivity in next-generation sensing systems, including wearable devices and bioelectronics. Such advanced platforms require highly conductive, biocompatible, and mechanically robust architectures capable of conforming to dynamic surfaces. Conventional metallic thin-film fabrication techniques have reached their fundamental physicochemical limits, often suffering from suboptimal mechanical strength, complex multi-step processing, and high costs. In contrast, additive manufacturing methodologies offer streamlined microfabrication, yet traditional printing methods frequently struggle with low-viscosity constraints, insufficient metal loading, and significant material losses. This paper covers the morphological fidelity, mechanical resilience, and electrical performance of rheologically tailored, high-concentration (above 90%) gold nanoparticle paste deposited via Ultra-Precise Dispensing (UPD) technology. The capability of the UPD system to print complex, high-density fractal geometries with linewidths down to 5 μm is evaluated on both rigid and flexible substrates, glass and polyimide, respectively. The mechanical structural integrity of these conductive traces is characterized under initial 360-degree bending tests. Finally, the electrical stability and thermal response of a printed proof-of-concept temperature sensor are evaluated. The printed fractal microstructures exhibit good resolution and the fabricated sensor demonstrates good stability, displaying a linear thermal response with a temperature coefficient of resistance of 1.98·10⁻³ °C⁻¹, validating this combined material-deposition approach for microelectronics. Full article

Additive manufacturing (AM), also known as three-dimensional (3D) printing, has emerged as a revolutionary digital near-net-shape manufacturing technology, offering innovative solutions for the design and fabrication of complex, high-performance structures and equipment. This paper reviews the recent advancements and applications of metal AM technologies in the marine sector. Firstly, the principles and characteristics of three most widely adopted metal AM processes in this field are introduced: laser powder bed fusion (L-PBF), directed energy deposition (DED), and wire arc additive manufacturing (WAAM). Subsequently, the application status of metal AM is summarized in four key marine sectors: propulsion systems, underwater vehicle housings and structures, hull structures and shipboard equipment and components, as well as marine equipment repair and emergency support. Building on this, the major challenges for metal AM applications in the marine environment are further discussed, including the fabrication of large-scale components, standardization of materials and processes, integration of smart manufacturing and digital technologies, and sustainability and circular manufacturing. Finally, future trends are projected toward higher efficiency, intelligence, and environmental sustainability. It is indicated that metal AM will fundamentally reshape the manufacturing mode of marine equipment and support its high-performance, low-cost, intelligent and rapid-response development. Full article

Additive manufacturing of NiTi shape memory alloys is challenging due to their sensitivity to composition and thermal history. The gap between high-resolution powder-based AM and high-productivity wire-based processes for NiTi remains a challenge. This study investigates the technical feasibility of depositing Ni-rich NiTi (56 wt.% Ni) using a micro gas metal arc-based directed energy deposition (µ-GMA-DED) process with a 300 µm wire. The investigation was conducted on a single-bead, single-layer geometry deposited onto a titanium substrate. The deposited layer exhibited a heterogeneous microstructure with dendritic and eutectic-like regions, where phase analysis revealed a mixture of NiTi and Ni₃Ti intermetallics. Differential scanning calorimetry showed suppression of the martensitic transformation in the as-deposited condition, likely due to the high fraction of non-transformable Ni₃Ti, compositional redistribution during rapid solidification, and potential substrate dilution. The nanoindentation results reflected this heterogeneity, with Young’s modulus ranging from 64 to 151 GPa. While post-deposition heat treatment partially restored the martensitic transformation, these results demonstrate the preliminary feasibility of the µ-GMA-DED process, noting that strict control over chemistry and dilution is required before the route can be applied to functional components. Full article

This paper reviews recent advances in additive manufacturing (AM) of aluminum alloys and proposes an integrated framework of materials, processes, microstructure, properties, and applications. Focusing on Laser Powder Bed Fusion (L-PBF) and Directed Energy Deposition (DED), it summarizes the major challenges of aluminum alloy AM, including hot cracking, porosity, and anisotropy, together with corresponding optimization strategies. The paper particularly highlights three additive manufacturing-specific alloy systems—Sc/Zr microalloyed, heat-resistant eutectic, and transition-metal-strengthened aluminum alloys—and clarifies their composition design and strengthening mechanisms. Finally, future trends in intelligent manufacturing, integrated alloy process design, and green development are discussed, emphasizing the importance of interdisciplinary integration for large-scale industrial applications. Full article

This article focuses on the research and development of innovative polymer-concrete composites for the manufacture of precision machine tool frames and critical mechanical engineering components. The relevance of this work stems from the need to replace traditional cast iron and cement concrete with materials with superior damping properties and thermal stability. The polymer matrix used in this study was ED-20 epoxy-diane resin, modified with (FAM) furan resin and cured with polyethylenepolyamine (PEPA), which together ensured minimal linear shrinkage (less than 0.5–1%) during polymerization. The focus was on the effect of multimodal filler distribution, including quartz sand, gabbro, and basalt, as well as reinforcing additives such as silicon carbide and fiberglass, on the final performance characteristics of the material. Experimental studies determined the key physical and mechanical parameters of the obtained samples. The results showed that the optimized composition (Smp_001) exhibited compressive strength up to 92.3 MPa, significantly exceeding that of standard high-strength concrete. It was established that the use of silicon carbide and glass fiber promotes the formation of a dense heterogeneous microstructure characterized by extremely low porosity (1.2–2.5%) and record-low water absorption (less than 0.05%). These characteristics guarantee high dimensional stability of the frames during prolonged contact with process fluids and cutting fluids. The scanning electron microscopy (SEM) and (EDS) energy dispersive X-ray spectroscopy methods confirmed the dense packing and high degree of interaction of the polymer matrix with the crystalline phases of the filler. This condition of the interfacial boundaries guarantees stable stress transfer throughout the entire volume of the material, which minimizes the risk of local damage during operation. The study confirmed that the developed material has vibration damping properties 6–10 times more effective than gray cast iron, a critical factor in improving machining accuracy on modern metal-cutting machines. The scientific novelty of the study lies in its substantiation of the synergistic effect of the combined use of basalt fillers and silicon carbide to achieve the precision properties of a structural material. Its practical significance is confirmed by the possibility of producing large-scale parts by casting without the need for complex finishing, opening up new prospects for modernizing the machine tool industry. Full article

Wire Arc Additive Manufacturing (WAAM) has emerged as a cost-effective and high-deposition-rate technique for fabricating large-scale metallic components; however, the complex thermal history inherent to the process leads to heterogeneous microstructures that can significantly influence tribological performance. In this study, the dry sliding wear behavior of WAAM-fabricated austenitic stainless steel 308L (SS308L) was systematically investigated using a pin-on-disk configuration. The influence of applied normal load (1.5–15 N) and sliding speed (0.03–0.229 m/s) on wear volume, specific wear rate, coefficient of friction (COF), and tangential force was evaluated. Optical microstructural observations indicated features consistent with a ferritic–austenitic solidification structure, including regions resembling polygonal ferrite, Widmanstätten ferrite, and austenitic dendritic morphologies. Wear results showed that wear volume and cross-sectional area increased monotonically with increasing load, while the effect of sliding speed was comparatively less significant. The specific wear rate remained on the order of 10⁻⁴ mm³/N·m with minor variations across test conditions. The COF decreased with increasing load up to 10 N, followed by a speed-dependent response at higher loads. The findings demonstrate that load is the dominant factor governing wear behavior in WAAM SS308L, while microstructural heterogeneity may contribute to frictional stability and wear resistance. This study provides valuable insight into the structure–tribology relationship of WAAM stainless steels and supports the optimization of process parameters for wear-critical applications. Full article