Search Results (933)

Large-Format Additive Manufacturing (LFAM) offers the ability to 3D print composites at multi-meter scale and high throughput by utilizing a screw-based extrusion system that is compatible with pelletized feedstock. As such, LFAM systems like the Big Area Additive Manufacturing (BAAM) system provide a pathway for incorporating AM techniques into industry-scale production. Despite significant growth in LFAM techniques and usage in recent years, typical Multi-Material (MM) techniques induce weak points at discrete material boundaries and encounter a higher frequency of delamination failures. A novel dual-hopper configuration was developed for the BAAM platform to enable in situ switching between material feedstocks that creates a graded transition region in the printed part. This research studied the influence of extrusion screw speed, component design, transition direction, and material viscosity on the transition behavior. Material transitions were monitored using compositional analysis as a function of extruded volume and modeled using a standard Weibull cumulative distribution function (CDF). Screw speed had a negligible influence on transition behavior, but averaging the Weibull CDF parameters of transitions printed using the same configurations demonstrated that designs intended to improve mixing increased the size of the blended material region. Further investigation showed that the relative difference and change in complex viscosity influenced the size of the blended region. These results indicate that tunable properties and material transitions can be achieved through selection and modification of composite feedstocks and their complex viscosities. Full article

This paper proposes a grating Moiré fringe signal compensation method based on Variational Mode Decomposition (VMD) to address signal errors in grating encoders. VMD decomposes Moiré fringe signals into multiple amplitude-modulated and frequency-modulated components, and realizes noise compensation through parameter optimization and signal reconstruction. The Multi-Strategy Particle Swarm Optimization (MSPSO) enhances optimization performance via adaptive inertia weight adjustment and chaotic perturbation, solving the problems of mode mixing or over-decomposition caused by blind parameter selection in traditional VMD. A hardware-software co-design test system based on ZYNQ FPGA is developed, which optimally allocates tasks between the Processing System and Programmable Logic, resolving issues of large data volume and long computation time in traditional systems. The compensation scheme provides excellent signal processing performance. The experimental tests on random periodic signals, triangular waves and square waves with different duty cycles have demonstrated the robustness of this scheme. After compensation, the output signal exhibits excellent sinuosity and orthogonality, with harmonic components and noise in the frequency domain almost negligible. It provides a practical solution for high-precision measurement in ultra-precision machining, semiconductor manufacturing, and automated control. Full article

Confronting severe water scarcity challenges, China’s industrial water circularity demands robust efficiency evaluation frameworks. This research pioneers a two-stage network model integrating undesirable outputs and feedback mechanisms to assess 30 provincial systems. The methodology captures interconnected processes where production generates wastewater, which is then treated to yield reusable water fed back into production. Comprehensive efficiency gaps were quantified using weighted optimization, enabling tailored provincial enhancement paths: wastewater volume reduction and reclaimed water augmentation strategies. Results reveal striking regional disparities, with only two regions initially achieving full efficiency while coastal manufacturing hubs exhibited paradoxical inefficiency despite high output. Implementation demonstrated reclaimed water enhancement’s superior efficacy—enabling over half of regions to reach full efficiency—while wastewater reduction alone proved insufficient for most provinces. Crucially, ecologically fragile regions achieved optimal performance through minimal precision interventions. The study establishes that effective water circularity requires coordinated optimization of both production and treatment stages, with region-specific sequencing strategies. This approach delivers policymakers a diagnostic toolkit for spatially differentiated resource transition planning, balancing economic output with environmental sustainability. Full article

Improving responsiveness and efficiency in production systems requires an understanding of how manufacturing flexibility and inventory management interact under conditions of uncertainty. This study examines the combined effect of four types of flexibility, machine, labor, routing, and volume, together with the use of buffers, on the total flow time of production batches. A total of 84 experimental configurations were simulated, of which 35 were feasible and statistically valid, using a discrete-event simulation model developed in Arena and validated with industrial data. The results show that combining high machine and labor flexibility reduces total flow time from 5450 to 3050 min (a 44% decrease), whereas routing and volume flexibility exhibit minor effects. Moreover, the inclusion of buffers further improves performance, reducing times by approximately 1000 min in low-flexibility configurations. These findings provide robust quantitative evidence to guide the design of adaptive production systems by jointly evaluating the flexibility and inventory management dimensions that are typically studied in isolation. Full article

This study presents the development and validation of a comprehensive numerical optimisation methodology used to improve the energy efficiency of a pump with normal characteristics: volume flow rate, Q nom = 6.3 m³/h, and head, H = 20 mH₂O. The methodology was implemented in ANSYS Workbench using ANSYS CFX and optiSLang. The optimisation process is based on data from 853 RANS (SST) calculations on a sample generated by the LHC method, varying the parameters of the blades and flow path. Response surfaces (RSM) were constructed using anisotropic and classical kriging, which were optimised using an Evolutionary Algorithm (EA). The optimised geometry was verified numerically by URANS SST and experimentally. For physical validation, the wheel was manufactured using SLS technology from PA-12 Industrial powder, a strength assessment FSI was performed, and the geometry was checked by 3D scanning. 3D scanning showed a high manufacturing accuracy (deviations of 0.1–0.3 mm). The result is a geometry that increases efficiency while maintaining head, which has been confirmed by experimental validation. Full article

Droplet microfluidics enables high-throughput, compartmentalized reactions using minimal reagent volumes, but most implementations rely on precision-fabricated chips and external pumping systems that limit portability and accessibility. Here, we present a handheld vibrational droplet generator that repurposes a consumer electric toothbrush and a modified disposable pipette tip to produce nearly monodisperse water-in-oil droplets without microfluidic channels or syringe pumps. The device is powered by the toothbrush’s built-in motor and controlled by a simple 3D-printed adapter and adjustable counterweight that tune the vibration amplitude transmitted to the pipette tip. By varying the aperture of the pipette tip, droplets with diameters from ~100–300 µm were generated at rates of ~100 droplets s⁻¹. Image analysis revealed narrow size distributions with coefficients of variation below 5% in typical operating conditions. We further demonstrate proof-of-concept applications in digital loop-mediated isothermal amplification (LAMP) and microbiological colony-forming unit (CFU) assays. A commercial feline parvovirus (FPV) kit manufactured by Beyotime Biotechnology Co., Ltd. (Shanghai, China), three template concentrations yielded emulsified reaction droplets that remained stable at 65 °C for 45 min and produced distinct fractions of fluorescent-positive droplets, allowing estimation of template concentration via a Poisson model. In a second set of experiments, the device was used as a droplet-based spreader to dispense diluted Escherichia coli suspensions onto LB agar plates, achieving uniform colony distributions across the plate at different dilution factors. The proposed handheld vibrational generator is inexpensive, easy to assemble from off-the-shelf components, and minimizes dead volume and cross-contamination because only the pipette tip contacts the sample. Although the current prototype still exhibits device-to-device variability and moving droplets in open containers complicate real-time imaging, these results indicate that toothbrush-based vibrational actuation can provide a practical and scalable route toward “lab-in-hand” droplet assays in resource-limited or educational settings. Full article

Large-format additive manufacturing (LFAM) is a manufacturing process in which high volumes of material are extruded in a layer-by-layer fashion to create large structures with often complex geometries. The Loci-One system, operated and developed by Loci Robotics Inc., is an LFAM-type system that was used to print single-bead walls of 20% by weight carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS) using various print parameter inputs. This study observed the influence of bead width and layer time on thermomechanical performance via material characterization techniques that accounted for the complex microstructure of LFAM parts to develop a better understanding of parameter–structure–property relationships. Printed parts were characterized by measuring the coefficient of thermal expansion (CTE) and interlayer strength. Near the edges of the printed beads, microscopy revealed a “thinning effect” experienced by a shell composed primarily of highly oriented fiber as the bead width was increased; however, this effect was diminished with a higher shear rate. The CTE results demonstrated the influence of mesostructure on the thermomechanical response. Increased shear rates were expected to lower CTE in the x-direction due to a higher ratio of fiber oriented in the print direction, but this relationship was not always observed. For the larger bead widths printed at higher shear rates, the randomly oriented fiber at the core dominated the thermomechanical response and increased CTE overall in the x-direction. A heat transfer model was developed for this work to determine how much time was required for the deposited bead to cool to the glass transition temperature. Interlayer strength results revealed a rapid decrease once the printed layer time exceeded the time required for the extrudate to cool below the glass transition temperature. Full article

Evacuated blood collection tubes are widely used in clinical and laboratory settings due to their simplicity and reliability. However, their performance is influenced by factors such as ambient pressure, temperature, tube design, and procedural conditions. This study systematically investigates and quantifies these effects on draw volume and filling dynamics, with a particular emphasis on high-altitude applications. A combination of theoretical modeling, experimental validation, and qualitative analysis was employed to identify critical parameters and assess their significance. The results demonstrate that standard tubes designed for sea-level conditions, particularly those with low fill ratios, may exhibit substantial deviations in draw volume at high altitudes. Factors such as blood temperature and venous pressure were found to have a considerable impact, while others, such as material creep, were negligible under typical conditions. By consolidating and analyzing these effects, this study provides a valuable resource for manufacturers and medical personnel, offering valuable insights to improve the design and use of evacuated blood collection tubes. The findings emphasize the importance of considering environmental conditions during production and clinical application, particularly for high-altitude scenarios. Future work should refine the models and expand testing under realistic conditions to enhance reliability and applicability. Full article

This study investigates the interlayer properties and sustainability of 3D-printed ultra-high-performance concrete (UHPC) modified with antimony tailings (ATs). The different AT ratios considered were 2.7, 5.4, 8.1, 10.8, and 13.5 wt% additions. The mechanical experiments show the optimal concentration resulting in compressive and flexural strength of 11.2% and 17.2% enhancement at 28 days, respectively. SEM analysis revealed that AT enhances the interlayer strength of 3D-printed UHPC and influences the anisotropic behavior of the matrix around steel fibers. X-CT demonstrated that increasing the AT from the compared group to 13.5% reduced the pore volume from 2.02% to 0.30%. Furthermore, an environmental impact assessment of the 10.8 wt% AT exhibited a 32.5% reduction in key indicators including abiotic depletion (ADP), acidification potential (AP), global warming potential (GWP), and ozone depletion potential (ODP). Consequently, UHPC incorporating AT offers superior environmental sustainability in the practical construction of 3D-printed concrete. This research provides practical guidance in optimizing 3D-printed UHPC engineering, further facilitating the integrated design and manufacturing of multi-layer structures. Full article

With the rapid development of the new energy vehicle industry, the efficiency of information sharing in the power battery recycling supply chain greatly affects resource utilization and sustainability. This paper examines battery manufacturers and third-party recyclers as game participants. We analyze incentive mechanisms for sharing technical information, considering both information quality and leakage risks. This study constructs three types of Stackelberg game models: contract mechanisms, profit-sharing mechanisms, and cost-sharing mechanisms. We analyze the impact of technical information quality and leakage costs on supply chain decisions. Results show that manufacturer profits increase with growing leakage costs, following optimal transitions through profit-sharing, contract, and cost-sharing mechanisms. Recycler profits are influenced by both the quality of technical information and leakage costs. Overall supply chain profits trend toward cost-sharing mechanisms when technical information quality is low and favor profit-sharing mechanisms when quality is high. Under low leakage risk, cost-sharing mechanisms dominate at the technological level and in terms of recycling quantity. Under high leakage risk, profit-sharing mechanisms share leakage costs and lead in technology investment and recycling quantity. Contract mechanisms consistently have the lowest levels and volumes because they lack cost sharing and profit compensation. This study provides a theoretical foundation and practical guidance for information-sharing strategies in power battery recycling supply chains. Full article

This work presents a highly compact and scalable 1.2-kV SiC MOSFET half-bridge building-block module enabled by a die-integrated 3D PCB packaging technology. Compared with conventional DBC-based or TO-247-based SiC half-bridge modules, the proposed design reduces the physical volume and weight by more than 90% while maintaining full compatibility with standard PCB manufacturing processes. The vertically laminated DC+/DC− conductors and symmetric PCB–die–PCB stack establish a tightly confined commutation loop, resulting in a measured power-loop inductance of 2.2 nH and a 3.8 nH gate-loop inductance—representing up to 94% and 89% reduction relative to discrete device implementations. Because the parasitic parameters are intrinsically well-balanced across replicated units and the mutual inductance between adjacent modules remains extremely small, the structure naturally supports current sharing during parallel operation. Thermal and insulation evaluations further confirm the suitability of copper filling via high-Tg laminated PCB substrates for high-power SiC applications, achieving withstand voltages exceeding twice the rated bus voltage. The proposed module is experimentally validated through finite-element parasitic extraction and 950 V double-pulse testing, demonstrating controlled dv/dt behavior and robust switching performance. This work establishes a manufacturable and parallel-friendly packaging approach for high-density SiC power conversion systems. Full article

Optimizing end mill geometry is critical for improving performance and reducing costs in the high-volume manufacturing of tools, dies and molds. This study demonstrates a successful optimization framework for solid end mills machining 1.2379 cold-work tool steel, integrating Finite Element Analysis (FEA), Artificial Neural Networks (ANN), and Genetic Algorithms (GA). The optimized tool geometry, derived from four key design parameters, delivered substantial performance gains over an industrial reference (parent) tool. Our ANN-GA model achieved a remarkable predictive accuracy (R = 0.75–0.98) over the RSM model (R = 0.17–0.63) and identified an optimal design that reduced the resultant cutting force by approximately 11% (to 142.8 N) and improved surface roughness by 21% (to 0.1637 µm) compared to experimental baselines. Crucially, the new geometry halved the tool breakage rate from 50% to ~25%. Parameter analysis revealed the width of the land as the most influential geometric factor. This work provides a validated, high-performance tool design and a powerful modeling framework for advancing machining efficiency in tool, mold and die manufacturing. Full article

Future fusion power plants require structural materials that can withstand extreme operating conditions, including high coolant outlet temperatures, mechanical loading, and radiation damage. Reduced-activation ferritic martensitic (RAFM) steels are a primary candidate as a structural material for such applications. This study demonstrates the successful production of a 5.5-tonne RAFM billet via electric arc furnace (EAF) technology, enabling scalable, cost-effective manufacturing. The resulting UK-RAFM alloy offers superior tensile strength and creep lifetime performance compared to Eurofer97. This is attributed to alterations in the initial forging process during manufacture. Modified thermomechanical treatments (TMTs) were subsequently applied to the UK-RAFM, which are shown to enhance the tensile strength further, particularly at 650 °C. Building on this, an Advanced RAFM (ARAFM) steel was designed to exploit the benefits of optimised chemistry to encourage metal carbonitride (MX) precipitate evolution alongside bespoke TMTs. Challenges around ensuring suitable processing windows in these steels, to avoid the over-coarsening of MX precipitates or the formation of deleterious delta-ferrite, are discussed. A subsequent 5.5-tonne ARAFM billet has since been produced using EAF facilities, with performance to be reported separately. This work highlights the synergy between alloy design, process optimisation, and industrial scalability, paving the way for a new generation of low-cost, high-volume, fusion-grade steels. Full article

As high-end equipment manufacturing advances, demand for improved surface performance in critical components has increased. Laser cladding is an advanced surface strengthening technique that affords effective surface modification. During the laser cladding process, obtaining a fine grain microstructure usually helps to enhance the microhardness, wear resistance, and corrosion resistance of the cladding layer. However, conventional laser cladding often yields coarse columnar grains that limit further performance improvements, so process optimization to achieve grain refinement is necessary. In this study, oscillating laser cladding was combined with a pulsed-wave (PW) laser mode to deposit a fine-grained Al₂CrFe₂Ni₄Ti_1.5 high-entropy alloy cladding on Q550 steel substrates. Compared with continuous-wave (CW) laser cladding, the PW mode produced markedly refined grains and concomitant improvements in microhardness, wear resistance, and corrosion resistance. Specifically, the microhardness of the PW cladding layer reached approximately 673.34 HV_0.5, the wear volume was approximately 0.06 mm³, the wear rate was approximately 0.21 × 10⁻⁴ mm³/N·m, and the corrosion current density decreased to approximately 1.212 × 10⁻⁵ A·cm⁻². This work presents a novel approach for producing high-performance, wear-resistant, and corrosion-resistant high-entropy alloy cladding layers, and offers both theoretical insight and potential engineering applications. Full article

In light of ongoing advancements in smart manufacturing, there is a growing need for intelligent fault diagnosis methods that maintain reliability under noisy, high-variability operating conditions. Conventional feature selection strategies often struggle when data contain outliers or suboptimal feature subsets, limiting their diagnostic utility. This study introduces a density-based feature space optimization (DBFSO) framework that integrates feature selection with localized density estimation to enhance feature space separability and classifier efficiency. Using k-nearest neighbor density estimation, the method identifies and removes low-density feature vectors associated with noise or outlier behavior, thereby sharpening the feature space and improving class discriminability. Experiments using roll-to-roll (R2R) manufacturing data under mechanical disturbances demonstrate that DBFSO improves classification accuracy by up to 36–40% when suboptimal feature subsets are used and reduces training time by 60–71% due to reduced feature space volume. Even with already-optimized feature sets, DBFSO provides consistent performance gains and increased robustness against operational variability. Additional validation using a bearing fault dataset confirms that the framework generalizes across domains, yielding improved accuracy and significantly more compact, noise-resistant feature representations. These findings highlight DBFSO as an effective preprocessing strategy for intelligent fault diagnosis in intelligent manufacturing systems. Full article