Search Results (196)

While material extrusion via fused filament fabrication (FFF) offers design flexibility and rapid prototyping, its practical use in engineering is limited by mechanical challenges, including residual stresses, geometric distortions, and potential interlayer debonding. These issues arise from the dynamic thermal profiles during FFF, including temperature gradients, non-uniform hardening, and rapid thermal cycling, which lead to uneven internal stress development depending on fabrication parameters and object topology. These problems can compromise the structural integrity and mechanical properties of FFF parts, especially when the load-bearing capacity and geometric accuracy are critical. This study focuses on polylactic acid (PLA) due to its widespread application in engineering. It introduces a computational framework for coupled thermo-mechanical simulations of the FFF process using ABAQUS (Version 2020) finite element software. A key innovation is an automated subroutine that converts G-code into a time-resolved event series for finite element activation. The simulation framework explicitly models the sequential stages of printing, cooling, and detachment, enabling prediction of adhesive loss and post-process warpage. A transient thermal model evaluates the temperature distribution during FFF, providing boundary conditions for a mechanical simulation that predicts residual stresses and warping. Uniquely, the proposed model incorporates the detachment stage, enabling a more realistic and experimentally validated prediction of warpage and residual stress release in FFF-fabricated components. Although the average deviation between predicted and measured displacements is about 10.6%, the simulation adequately reflects the spatial distribution and magnitude of warpage, confirming its practical usefulness for process optimization and design validation. Full article

Three-dimensional (3D) bioprinting has emerged as a versatile platform in regenerative medicine, capable of replicating the structural and functional intricacies of the central and peripheral nervous systems (CNS and PNS). Beyond structural repair, it enables the construction of engineered tissues that closely recapitulate neural microenvironments. This review provides a comprehensive and critical synthesis of current bioprinting strategies for neural tissue engineering, with particular emphasis on comparing natural, synthetic, and hybrid polymer-based bioinks from mechanistic and translational perspectives. Distinctively, it highlights gradient-based modulation of Schwann cell behavior and axonal pathfinding using mechanically and chemically patterned constructs. Special attention is given to printing modalities such as extrusion, inkjet, and electrohydrodynamic jet printing, examining their respective capacities for controlling spatial organization and microenvironmental cues. Representative applications include brain development models, neurodegenerative disease platforms, and glioblastoma scaffolds with integrated functional properties. Furthermore, this review identifies key translational barriers—including host tissue integration and bioink standardization—and explores emerging directions such as artificial intelligence-guided biofabrication and organ-on-chip integration, to enhance the fidelity and therapeutic potential of neural bioprinted constructs. Full article

Residual stresses and internal strains in 3D printing can lead to issues such as cracking, warping, and delamination—challenges that are amplified when using functional composite materials like magnetic PLA filaments. This study investigates the thermo-mechanical strain evolution during fused filament fabrication (FFF) of magnetite-filled PLA using an integrated methodology combining strain gauge sensors, high-resolution infrared thermal imaging, and synchrotron X-ray microtomography. Printing parameters, including nozzle temperature (190–220 °C), build platform temperature (30–100 °C), printing speed (30–60 mm/s), and cooling strategy (fan on/off) were systematically varied to evaluate their influence. Results reveal steep thermal gradients along the build direction (up to −1 °C/µm), residual strain magnitudes reaching 0.1 µε, and enhanced viscoelastic creep at elevated platform temperatures. The addition of magnetic particles modifies heat distribution and strain evolution, leading to strong sensitivity to process conditions. These findings provide valuable insight into the complex thermo-mechanical interactions governing the structural integrity of magnetically functionalized PLA composites in additive manufacturing. Full article

Additive manufacturing offers new perspectives for creating complex geometries with improved design features at lower cost and with reduced manufacturing time. It may even become possible to print a micro-turbojet engine in one single print, but then unconventional geometrical constraints on compressor and turbine designs are inevitable. If a radial machine were printed through additive manufacturing as a standalone component, the most logical print direction would be from the radial outlet/inlet to the axial inlet/outlet to ease the process and limit the supports, with limited additional constraints compared to traditional manufacturing methods. If the rotor comprising a radial compressor and turbine needs to be printed in one single print, one of the components will be printed in a direction that is not favorable. In the present work, the radial turbine is considered to be printed in the unfavorable direction, namely, from the axial outlet to the radial inlet. These geometrical constraints orient the geometry towards a mixed-flow configuration with a trailing-edge cutback. Such design features reduce the available design space for improvement and will clearly have an unfavorable impact on performance. Therefore, a multi-disciplinary gradient-based adjoint optimization of the mixed-flow turbine is performed, striving to limit the adverse impact on total-to-total efficiency while respecting the mass flow rate and power matching with the upstream compressor. The structural constraint limits the p-Norm von Mises stress to a maximum threshold based on the material yield strength at the operating temperature. The results show that a satisfactory compromise can be found between manufacturability constraints, material limits and aerodynamic performance. Full article

With the rapid advances of the electronics industry, a large amount of acidic etching waste solutions (AEWS) for etching Printed Circuit Board (PCB) are generated, which require complete remediation and sustainable recycling to avoid environmental pollution and wasting of resources. Herein, the novel purification technology for the acidic copper-containing etching waste solution was exploited via integrated alkali gradient pH control (3.0, 3.2, and 3.5). At pH 3.0, the system demonstrated selective metal removal with 94.02% efficiency for Fe and 82.60% for Mn. Elevating the pH to 3.2 enabled effective elimination of Zn (59.32%), Cr (59.46%), and Al (33.24%), while maintaining minimal copper loss (8.16%). Further pH adjustment to 3.5 achieved enhanced removal efficiencies of 97.86% (Fe), 91.30% (Mn), 59.38% (Zn), 62.10% (Cr), 21.66% (Ca), 34.05% (Al), and 26.66% (Co), with copper retention remaining high at 70.83% (29.17% loss). Furthermore, using the purified AEWS (pH 3.2) as precursor, high-purity nano-CuO was successfully synthesized through a Cu₂(OH)₃Cl conversion-calcination process, exhibiting 99.20% CuO purity with 0.0012% chlorine content and <0.1% metallic impurities. The development and application of the purification technology for AEWS containing copper, along with the production methodology for high-purity CuO, were significant to the fields of electronic information industry, environmental engineering, green industry and sustainable development of the ecological environment. Full article

The present study investigates the tensile properties of 3D-printed PA12 composites reinforced with short carbon fibers, focusing on the impact of printing parameters on material performance. We employed both experimental testing and machine learning-based predictive modeling to evaluate the influence of layer thickness, extrusion width, and raster angles on failure stress, failure strain, and stress–strain curves. Four machine learning models, including Gaussian process regression (GPR), gradient boosting regression (GBR), random forest (RF), and artificial neural network (ANN), were developed and trained on the experimental data. The results indicated that ANN and GPR models outperformed RF and GBR in predicting mechanical properties, with ANN demonstrating the highest accuracy across all tasks. A SHAP analysis was conducted to interpret the models, revealing that raster angles significantly influence failure stress predictions, while extrusion width predominantly affects failure strain predictions. The ability of the models to predict entire stress–strain curves provides a comprehensive understanding of the material’s mechanical behavior, which is crucial for applications requiring detailed material response data. This study highlights the potential of machine learning models, particularly ANN, in predicting the tensile properties of 3D-printed composites. The findings offer valuable insights for optimizing the 3D printing process to achieve desired material characteristics and pave the way for further research in integrating these predictive tools into additive manufacturing workflows for real-time optimization and quality control. Full article

Preformed porous media (PPM) technology has emerged as a transformative approach to enhance heat and mass transfer in vacuum freeze-drying (VFD) of agricultural and food products. This review systematically analyzes recent advances in PPM research, with particular focus on spray freeze-drying (SFD) as the dominant technique for precision pore architecture control. Empirical studies confirm PPM’s efficacy: drying time reductions of 20–50% versus conventional VFD while improving product quality (e.g., 15% higher ginsenoside retention in ginseng, 90% enzyme activity preservation). Key innovations include gradient porous structures and multi-technology coupling strategies that fundamentally alter transfer mechanisms through: resistance mitigation via interconnected macropores (50–500 μm, 40–90% porosity), pseudo-convection effects enabling 30% faster vapor removal, and radiation enhancement boosting absorption by 40–60% and penetration depth 2–3 times. While inherent VFD limitations (e.g., low thermal conductivity) persist, we identify PPM-specific bottlenecks: precision regulation of pore structures (<5% size deviation), scalable fabrication of gradient architectures, synergy mechanisms in multi-field coupling (e.g., microwave-PPM interactions). The most promising advancements include 3D-printed gradient pores for customized transfer paths, intelligent monitoring-feedback systems, and multiscale modeling bridging pore-scale physics to macroscale kinetics. This review provides both a critical assessment of current progress and a forward-looking perspective to guide future research and industrial adoption of PPM-enhanced VFD. Full article

In this work, bio-based thermoelectric composites were developed using acrylated epoxidized soybean oil (AESO) as the polymer matrix and bismuth telluride (Bi₂Te₃) as the thermoelectric filler. The materials were formulated for both UV-curing and thermal-curing processes, with a focus on Digital Light Processing (DLP) 3D printing. Although UV curing proved ineffective at high filler concentrations due to the light opacity of Bi₂Te₃, thermal curing enabled the fabrication of stable, homogeneously dispersed composites. The samples were thoroughly characterized through rheology, FTIR, TGA, XRD, SEM, and density measurements. Thermoelectric performance was assessed under a 70 °C temperature gradient, with Seebeck coefficients reaching up to 51 µV/K. Accelerated chemical degradation studies in basic media confirmed the degradability of the matrix. The results demonstrate the feasibility of combining additive manufacturing with sustainable materials for low-power thermoelectric energy harvesting applications. Full article

Automated parcel lockers (APLs) are transforming urban last-mile delivery by reducing failed distributions, decoupling delivery from recipient availability, optimizing carrier routes, reducing carbon foot-print and mitigating traffic congestion. The paper investigates the optimal design of APLs systems under stochastic demand and operational constraints, formulating the problem as a resource allocation optimization with service-level guarantees. We proposed a data-driven discrete-event simulation (DES) model implemented in ARENA to (i) determine optimal locker configurations that ensure customer satisfaction under stochastic parcel arrivals and dwell times, (ii) examine utilization patterns and spatial allocation to enhance system operational efficiency, and (iii) characterize inventory dynamics of undelivered parcels and evaluate system resilience. The results show that the configuration of locker types significantly influences the system’s ability to maintain high customers service levels. While flexibility in locker allocation helps manage excess demand in some configurations, it may also create resource competition among parcel types. The heterogeneity of locker utilization gradients underscores that optimal APLs configurations must balance locker units with their size-dependent functional interdependencies. The Dickey–Fuller GLS test further validates that postponed parcels exhibit stationary inventory dynamics, ensuring scalability for logistics operators. As a theoretical contribution, the paper demonstrates how DES combined with time-series econometrics can address APLs capacity planning in city logistics. For practitioners, the study provides a decision-support framework for locker sizing, emphasizing cost–service trade-offs. Full article

This study investigates the mechanical properties of ABS parts fabricated via used deposition modeling (FDM) through integrated experimental and numerical approaches. ABS resin was used as the experimental material, and tensile tests were conducted using a universal testing machine. Finite element analysis (FEA) was performed via ANSYS 2021 to simulate stress deformation behavior, with key parameters including a gauge length of 10 mm (pre-stretching) and printing temperature gradients. The results show that the specimen exhibited a maximum tensile force of 7.3 kN, upper yield force of 3.7 kN, and lower yield force of 3.2 kN, demonstrating high strength and toughness. The non-proportional elongation reached 0.06 (6%), and the quantified enhancement multiple of AM relative to traditional manufacturing was 1.1, falling within the reasonable range for glass fiber-reinforced or specially formulated ABS. FEA results validated the experimental data, showing that the material underwent 15 mm of plastic deformation before fracture, consistent with ABS’s ductile characteristics. Full article

This paper presents three realizations of a complete set with a horn antenna and a focusing Gradient Index (GRIN) lens in X-band. The set was specifically designed for advancing additive manufacturing (AM) of polymers with different materials and techniques. The set has three constituent parts: a horn antenna, a support, and a lens. The horn antenna is the active element and must be electrically conductive; it was manufactured with Rigid10K acrylic resin and subsequently metallized using an electroless process. The support needed to be light, robust, and electrically transparent, so that Polyamide 11 (PA11) was used. The lens realization was intended for a dielectric material whose permittivity varies with its density. Therefore, the dielectric permittivity and loss tangent of different polymeric materials used in AM at 2.45, 6.25, and 24.5 GHz were measured. In addition, stochastic and gyroid mesh structures have been studied. These structures allow for printing a volume that presents porosity, enabling control over material density. Measuring the dielectric characteristics of each material with each density enables the establishment of graphs that relate them. The sets were then manufactured, and their frequency response and radiation diagram were measured, showing excellent results when compared with the literature. Full article

We developed biomimetic full-thickness artificial skin using stromal vascular fraction (SVF) cells and autologous keratinocytes for the dermal and epidermal layers of skin, respectively. Full-thickness artificial skin scaffolds were fabricated using 4% porcine collagen and/or elastin in a low-temperature three-dimensional printer. Two types of scaffolds with collagen-to-elastin ratios of 100:0 and 100:4 were printed and compared. The scaffolds were analyzed for collagenase degradation, tensile strength, and structural features using scanning electron microscopy. By 24 h, the collagen-only scaffolds showed gradual degradation, and the collagen-elastin scaffolds retained the highest structural integrity but were not degraded. In the tensile strength tests, the collagen-only scaffolds exhibited a tensile strength of 2.2 N, while the collagen-elastin scaffolds showed a tensile strength of 4.2 N. Cell viability tests for keratinocytes displayed an initial viability of 89.32 ± 3.01% on day 1, which gradually increased to 97.22 ± 4.99% by day 7. Similarly, SVF cells exhibited a viability of 93.68 ± 1.82% on day 1, which slightly improved to 97.12 ± 1.64% on day 7. This study presents a novel strategy for full-thickness artificial skin development, combining SVF and keratinocytes with an optimized single collagen scaffold and a gradient pore-density structure. Full article

Additive manufacturing, particularly Fused Filament Fabrication (FFF), provides notable advantages such as design flexibility and efficient material usage. However, components produced via FFF often exhibit suboptimal surface quality and dimensional inaccuracies. Acrylonitrile Butadiene Styrene (ABS), a widely used thermoplastic in FFF applications, commonly necessitates post-processing to enhance its surface finish and dimensional precision. This study investigates the effects of CO₂ laser cutting on FFF-printed ABS plates, focusing on surface roughness, top and bottom kerf width, and bottom heat-affected zone. Forty-five experimental trials were conducted using different combinations of plate thickness, cutting speed, and laser power. Measurements were analysed statistically, and analysis of variance was applied to determine the significance of each parameter. To enhance prediction capabilities, seven machine learning models—comprising traditional (Linear Regression and Support Vector Regression), ensemble (Extreme Gradient Boosting and Random Forest), and deep learning algorithms (Long Short-Term Memory (LSTM), LSTM-Gated Recurrent Unit (LSTM-GRU), LSTM-Extreme Gradient Boosting (LSTM-XGBoost))—were developed and compared. Among these, the LSTM-GRU model achieved the highest predictive performance across all output metrics. Results show that cutting speed is the dominant factor affecting cutting quality, followed by laser power and thickness. The proposed experimental-computational approach enables accurate prediction of laser cutting outcomes, facilitating optimisation of post-processing strategies for 3D-printed ABS parts and contributing to improved precision and efficiency in polymer-based additive manufacturing. Full article

Moisture–electricity generators (MEGs) hold great promise for green energy conversion. However, existing devices focus on the need for complex gradient distribution treatments and the improvement in output voltage, overlooking the important role of the graphene oxide (GO) oxidation degree and the response time and recovery time in practical application. In this work, we develop printed MEGs by synthesizing reduced graphene oxide/silver nanoparticle (rGO/Ag) composites and controlling the GO oxidation degree. The rGO/Ag layer serves as a functional component that enhances cycling stability and shortens the recovery time. Additionally, compared to conventional rigid-structure devices, these flexible MEGs can be produced by inkjet printing and drop-casting techniques. A 1 cm² MEG can generate a voltage of up to 60 mV within 2.4 s. Notably, higher output voltages can be easily achieved by connecting multiple MEG units in series, with 10 units producing 200 mV even under low relative humidity (RH). This work presents a low-cost, highly flexible, lightweight, and scalable power generator, paving the way for broader applications of GO and further advancement of MEG technology in wearable electronics, respiratory monitoring, and Internet of Things applications. Full article

Printed circuit board (PCB) assembly, on the basis of surface mount electronic component welding, is one of the most important electronic assembly processes, and its defect detection is also an important part of industrial generation. The traditional two-stage target detection algorithm model has a large number of parameters and the runtime is too long. The single-stage target detection algorithm has a faster running time, but the detection accuracy needs to be improved. To solve this problem, we innovated and modified the YOLO11n model. Firstly, we used the Retention Block (RetBlock) to improve the C3K2 module in the backbone, creating the RetC3K2 module, which makes up for the limitation of the original module’s limited, purely convolutional local receptive field. Secondly, the neck structure of the original model network is fused with a Multi-Branch Auxiliary Feature Pyramid Network (MAFPN) structure and turned into a multi-branch auxiliary neck network, which enhances the model’s ability to fuse multiple scaled characteristics and conveys diverse information about the gradient for the output layer. The improved YOLO11n model improves its mAP50 by 0.023 (2.5%) and mAP75 by 0.026 (2.8%) in comparison with the primitive model network, and detection precision is significantly improved, proving the superiority of our proposed approach. Full article