Search Results (766)

Additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized polymer-based fabrication through design freedom and material efficiency. This work presents a comprehensive statical optimization of FDM parameters affecting the flexural properties of acrylonitrile/butadiene/styrene (ABS) specimens. The effects of layer thickness (0.15–0.25 mm), infill density (30–70%), printing speed (35–95 mm/s), and build orientation (Flat, On-edge, Vertical) were investigated following ASTM D790 standards. A Taguchi L9 orthogonal array coupled with ANOVA analysis was employed to quantity parameter significance. According to the ANOVA analysis, infill density was identified as the most influential parameter, accounting for 61.3% of the variation in flexural strength (σ_f) and 60.1% in flexural modulus (E_b). The optimal configuration (0.25 mm layer thickness, 70% infill, 65 mm/s speed, horizontal orientation) yielded a flexural strength of 84.9 MPa and modulus of 2.54 GPa. Microstructural observations confirmed that higher infill and moderate speed improved interlayer fusion and reduced void formation. The developed Taguchi–ANOVA framework offers quantitative insights for tailoring process–structure–property relationships in polymer-based additive manufacturing. Full article

To explore microbial resources for ester production in sub-high-temperature Daqu, this study first established that the esterifying enzyme activity in Daqu predominantly originated from fungi, with Rhizopus being the dominant fungal genus. Six Rhizopus strains capable of decomposing esters were isolated and purified from Daqu. Following secondary screening, strain M1 exhibited the highest esterification activity (40.26 U/mL) and was identified as Rhizopus oryzae based on morphological characteristics and molecular biological analyses. This strain was subsequently designated as Rhizopus oryzae M1 (R. oryzae M1). Using mycelial powder of strain M1 as the inoculum and sterilized wheat bran as the substrate, a pure-culture Fuqu was prepared. Orthogonal array design experiments were conducted to optimize the preparation process of this Fuqu, using esterifying enzyme activity as the evaluation index. Under the optimal conditions, the spore count and esterification activity of the pure-culture Fuqu reached 1.73 × 10⁹ CFU/g and 80.13 U/g, respectively. This pure-culture Fuqu was subsequently used as an inoculum to produce fortified Daqu. Following orthogonal optimization of the Daqu preparation process, the esterification activity of the fortified Daqu reached 103.22 U/g, and its key physicochemical indices met the requirements for high-quality sub-high-temperature Daqu. Analysis of the microbial community structure revealed that Rhizopus was the dominant fungal genus in the fortified Daqu, with its relative abundance increased by 35% compared to the non-fortified Daqu. Consistent with this, the esterifying enzyme activity of the fortified Daqu was 51.79% higher, suggesting that Rhizopus may have been largely responsible for the increase in esterification capacity. In laboratory-scale Baijiu brewing trials, this fortified Daqu produced a base Baijiu with a total ester content of 2.74 g/L, representing a 40.5% increase over the non-fortified Daqu and further confirming the pivotal role of Rhizopus in driving the esterifying enzyme activity. This study successfully screened a high esterifying enzyme-producing strain, R. oryzae M1, systematically optimized its enzyme production and Qu-making processes, and provides an excellent microbial strain and process reference for the preparation of fortified Daqu and the enhancement of Baijiu flavor. Full article

This study proposes a high-isolation four-port wideband MIMO antenna array designed for sub-6 GHz 5G, IoT, and radar applications. The array is fabricated on a polycarbonate substrate with overall dimensions of 500 × 500 mm² (ε_r = 2.8, h = 1 mm). Four orthogonally arranged modified circular patches with triangular ground planes and optimized inter-element spacing (D₁ = 90 mm) are employed in the antenna’s design to achieve an impedance bandwidth of 0.7–7.0 GHz (Fractional Bandwidth (FBW) > 163.63%) with |S_ii| < −10 dB across all ports. The measurement results indicate that the inter-port isolation is better than 15 dB (worst-case) across the 0.7–7 GHz band, exceeding 25 dB over 63.5% of the bandwidth (with a peak of approximately 50 dB); the envelope correlation coefficient (ECC) is ultra-low (<0.008); the total active reflection coefficient (TARC) is less than −10 dB for primary multi-port excitations; the mean effective gain (MEG) is balanced (≈−3 dB); and the group delay is consistent (~0.5 ns). With a maximum realized gain of 10 dBi, the antenna exhibits omnidirectional radiation patterns, showing a significant correlation between the simulation (CST Microwave Studio) and measurement results. The proposed antenna is particularly well-suited for use in high-throughput sub-6 GHz 5G base stations and wideband wireless systems, offering superior port isolation through multi-mode resonance without the need for metamaterials and outperforming existing four-port designs. Full article

Reliable property prediction in extrusion-based additive manufacturing remains challenging under extreme data scarcity (e.g., sample size of <50), particularly when experiments are constrained by designed studies such as Taguchi orthogonal arrays. In direct ink writing of lignocellulosic composites, limited experimental runs restrict the development of predictive models capable of guiding formulation and process optimization. This study introduces a physics-consistent data augmentation framework to enhance predictive reliability while preserving material-consistent behavior. Synthetic data are evaluated using four criteria: sensitivity to augmentation size, distributional consistency with experimental observations, stability with respect to boosting depth in regression modeling, and preservation of physics-consistent factor hierarchies through interpretability analysis. The framework is validated using compressive strength data from direct ink writing experiments conducted under an extremely small data regime. Results show that augmentation performance depends on the augmentation scale and model capacity. Variational autoencoder-based augmentation produced more stable and physically consistent predictions than conditional tabular generative adversarial networks in this application. Increasing predictive accuracy alone, or applying excessive augmentation, can distort material hierarchies and reduce physics consistency. The proposed evaluation framework supports reliable and interpretable property prediction in additive manufacturing when experimental data are severely limited. Full article

This study presents an integrated optimization framework that combines the Design of Experiments (DOE) approach with Machine Learning (ML) techniques to minimize defects in the injection molding of Fuel Vapor Charcoal Canisters. The research focuses on five critical process parameters—melt temperature, mold temperature, filling time, pressure holding time, and pure cooling time—whose combined influence on major molding defects (warpage, shrinkage, shear stress, residual stress, and short shots) was systematically investigated. A Taguchi L25 orthogonal array was employed to structure the experiments and identify the optimal parameter levels through signal-to-noise (S/N) ratio analysis using the “smaller-the-better” quality criterion. The Taguchi results revealed that pressure holding time was the most influential factor, followed by mold temperature and melt temperature. Simulation results from SolidWorks Plastics confirmed the reduction in major defects under the optimized settings. To further validate and generalize the DOE findings, a Random Forest regression model was trained on the same dataset to capture nonlinear interactions between parameters. The model achieved an average RMSE of 2.451 ± 0.591 in five-fold cross-validation, demonstrating strong predictive accuracy. Feature importance analysis indicated that pressure holding time accounted for approximately 77.5% of the variance in the defect index, reaffirming its dominant role. A 3D response surface of the global parameter space (mold temperature vs. pressure holding time) revealed a distinct minimum defect region, consistent with the DOE-optimized settings. The Taguchi analysis identified the optimal parameter settings as Melt Temperature at Level 2, Mould Temperature at Level 3, Filling Time at Level 4, Pressure Holding Time at Level 5, and Pure Cooling Time at Level 4, which collectively produced the highest S/N ratios and the lowest overall defect index. The overall discrepancy between DOE and ML predictions was only 12.5%, confirming methodological consistency. The integration of DOE and ML not only enhances parameter interpretability and defect prediction accuracy but also provides a scalable, data-driven approach for intelligent process control and quality assurance in automotive injection molding. Full article

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has emerged as a promising technique to enhance spectral efficiency and coverage in fifth- and sixth-generation wireless networks. However, asymmetric indoor propagation conditions characterized by heterogeneous line-of-sight (LoS) and non-line-of-sight (NLoS) links often degrade user fairness. This paper investigates a downlink RIS-assisted NOMA system under the standardized 3GPP indoor office (InH) channel model to address fairness-oriented design under realistic link-budget constraints. We formulate an optimization problem for max–min fairness that jointly considers discrete RIS element partitioning and NOMA power allocation to achieve a symmetrical allocation of quality of service (QoS). To enable efficient computation, the non-convex problem is transformed into an epigraph form and solved using a low-complexity, bisection-based quasi-convex optimization framework combined with enumeration over RIS partitions. Numerical results demonstrate significant fairness gains; for instance, doubling the RIS array size yields a substantial improvement in the ergodic max–min rate, corresponding to approximately a 66% gain at moderate transmit power levels. Furthermore, by accounting for practical impairments such as imperfect successive interference cancellation (iSIC), imperfect channel state information (iCSI), and RIS implementation losses, the results reveal that fairness-optimal operation consistently prioritizes the far user to overcome severe indoor NLoS attenuation. The proposed framework is also compared with alternating optimization (AO)-based RIS-NOMA, conventional RIS beamforming without partition and RIS-assisted orthogonal multiple access (OMA) schemes. Simulation results confirm that the proposed framework achieves low computational complexity, making it suitable for practical indoor wireless environments. Full article

This paper introduces a compact sub-6 GHz multiple-input multiple-output (MIMO) antenna array developed for 5G smartphone applications. The design employs eight planar inverted-F antenna (PIFA) elements arranged to realize dual-band and dual-polarized operation. The antenna achieves impedance bandwidths of 3.3–3.7 GHz (11.4%) and 5.3–5.8 GHz (10%), covering key sub-6 GHz fifth-generation (5G) bands. To enhance diversity performance, the elements are distributed along the edges of the smartphone mainboard, enabling excitation of orthogonal polarization modes while maintaining an overall board size of 75 mm × 150 mm on an FR4 substrate. Even without the use of dedicated decoupling structures, the closely spaced antenna elements exhibit satisfactory isolation levels, varying between −12 dB and −22 dB across the operating bands. The antenna array achieves wide impedance bandwidths of approximately 400 MHz at 3.5 GHz and more than 500 MHz at 5.5 GHz, supporting high data-rate communication. In addition, the proposed system demonstrates very low correlation and active reflection, with envelope correlation coefficient (ECC) values below 0.002 and total active reflection coefficient (TARC) levels better than −20 dB. User interaction effects are also investigated, and the results confirm acceptable SAR levels and stable radiation behavior in the presence of the human body. Owing to its planar, dual-band/dual-polarization capability and compliance with safety requirements, the proposed antenna represents a promising practical solution for contemporary 5G handheld devices and future multi-band mobile platforms. Full article

The research conducted in this paper is a practical example of the Design of Experiments methodology. In accordance with the assumptions of the experimental design, the authors drew attention to the problem: how should the spark plasma sintering process be planned to obtain the maximum amount of information needed to optimise the consolidation of the WC-6Co composite at the lowest possible cost? The DOE methodology—a powerful technique for investigating new processes and gaining knowledge about existing ones in order to optimise them for high performance—was employed in the study. The aim of the research was to optimise the consolidation of the spark-plasma sintering process of the WC-6Co composite using the DoE (Design of Experiments) methodology. Four sintering factors were selected for the study: sintering temperature (factor A, 1300–1400 °C); heating rate (factor B, 100–300 °C/min); sintering time (factor C, 150–600 s); and pressure (factor D, 40–50 MPa). Each consolidation factor was designed to cover three levels. The L9 orthogonal array was used. It was found that sintering temperature and heating rate had the greatest impact on apparent density. To validate the statistical model, sintering tests were performed at a temperature of 1380 °C, a heating rate of 100 °C/min, a sintering time of 150 s and a pressing pressure of 45 MPa. Validation analysis of the statistical model demonstrated consistency with the experimental results. The WC-6Co composite achieved an apparent density of 14.85 g/cm³, corresponding to 97.42% of the theoretical density, with a hardness of 1809 HV30 and total porosity of 2.583%. X-ray diffraction studies revealed the presence of tungsten carbide and cobalt in the structure. Full article

In frequency division duplex (FDD) massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, the sub-array multi-user (MU) hybrid beamforming architecture is highly attractive because of its low hardware cost and high energy efficiency. However, downlink channel state information (CSI) acquisition and hybrid beamformer optimization remain challenging due to the large feedback overhead and the non-convexity of the beamforming design. To address these issues, we propose an end-to-end deep learning (DL) framework that jointly optimizes pilot training, CSI feedback, and hybrid beamforming, overcoming the limitations of conventional independently designed modules. At the core of the network, we introduce the star efficient location attention (StarELA) module, which combines the implicit high-dimensional representation capability of star operations (element-wise multiplication) with the fine-grained feature localization of efficient location attention (ELA). In addition, for wideband digital beamformer generation, we exploit inter-subcarrier correlation and design a frequency–domain seed generation and interpolation upsampling strategy, which significantly reduces network parameters. Experimental results show that the proposed method approaches the upper-bound performance of conventional hybrid beamforming with ideal CSI, while consistently outperforming existing benchmark methods. Full article

There are numerous operating parameters that affect the thermodynamic and thermoeconomic performance of gas turbine cycles, and many studies based on energy, exergy, and economic analyses have been conducted in the literature by considering these parameters. However, the order of importance and contribution ratios of key operating parameters such as ambient temperature, compressor pressure ratio, combustion efficiency, regenerator effectiveness, and compressor and turbine isentropic efficiencies with respect to thermal efficiency, exergy efficiency, and the levelized cost of electricity (LCOE) have not been sufficiently investigated using statistical methods. Accordingly, a thermodynamic model of a gas turbine cycle improved with intercooling, reheating, and regeneration processes was developed in the study, and thermal efficiency, exergy efficiency, and LCOE values were calculated under different parameter levels. Taguchi analysis was carried out by using the L27 orthogonal array, in which six operating parameters were evaluated at three levels, and optimum parameter levels were determined for each performance indicator. Next, the contribution ratios of the parameters to the objective functions were calculated using the ANOVA method. The results showed that turbine isentropic efficiency was the most influential parameter in terms of thermal and exergy efficiencies, while compressor pressure ratio played the dominant role in terms of LCOE. Additionally, to simultaneously achieve the goals of maximizing thermal and exergy efficiencies and minimizing the LCOE value, the grey relational analysis (GRA) method was applied as a multi-objective optimization approach, and the optimum operating conditions were determined based on a single performance indicator. According to the GRA results, under the optimum conditions, the thermal efficiency was calculated as 0.5533, its exergy efficiency was 0.5772, and the LCOE value was 0.01751 USD/kWh. Full article

This study applies grey theory alongside fuzzy models based on Taguchi design experiments to optimise the performance of coatings for multiple responses, which enhances the quality of plasma-sprayed ceramic coatings. An L18 orthogonal array experiment with eight control factors was conducted, and the impact of the control parameters on the surface properties of the coatings was critically evaluated. In addition, an analysis of variance was conducted, and the surface structure of the coatings was examined using SEM. The multi-response characteristics of surface roughness, porosity, hardness, coating thickness and wear depth values during the spraying of ceramic coatings were studied comparatively through optimisation. In addition, a confirmation experiment was conducted. The experimental results show that surface roughness was reduced by 15.96%, porosity by 65.35%, hardness by 34.60%, wear depth by 34.04%, and coating thickness by 32.01% through optimal factors by plasma spraying coatings. Overall, a 48.94% improvement in multi-properties was observed when compared to the initial settings. Based on the above results, this study employed Taguchi methods to optimize the modeling of plasma spraying using grey-based fuzzy theory, thereby significantly enhancing the multi-response quality of plasma-sprayed coatings. The expected outcomes in terms of coating surface properties have also been achieved by these results. Full article

The jujube (Ziziphus jujuba Mill.) is a significant economic fruit tree, valued for its nutritional and medicinal properties. However, advances in functional genomics are hindered by the lack of an efficient transformation system. To overcome the limitations of conventional explant, we established a fruit-pulp-derived, callus-based Agrobacterium-mediated transformation system using fruit-pulp harvested 50 days after pollination. Through orthogonal experimental design, 6-benzylaminopurine and 2,4-dichlorophenoxyacetic acid were identified as key regulators for inducing high-quality, friable callus in two jujube genotypes, ‘JZ60’ and ‘LWCZ’. This system revealed significant genotype-specific variation in auxin requirements for callus proliferation and in differential antibiotic sensitivity. Transformation efficiency, as evaluated by fluorescence screening, was primarily determined by acetosyringone concentration and the binary vector architecture. The results revealed that the compact pCY (kanamycin resistance) vector achieved higher transformation efficiency (up to 77.8%) than pCAMBIA1301, whereas the pCAMBIA1301 (hygromycin resistance) vector enabled more uniform transgene expression. Integration and expression of the ZjCBF3 transgene were confirmed by polymerase chain reaction (PCR), reverse transcription quantitative PCR, and green fluorescent protein fluorescence assays. This study established a fruit-pulp-based callus transformation system for jujube, providing a rapid platform for its functional genomic studies. Full article

Fused Deposition Modeling (FDM) enables rapid, flexible production of polymer-based parts; however, because of additive manufacturing’s nature, it creates distinct microscale surface structures. These micro-scale surface morphologies directly affect the functional properties of the parts, such as surface roughness and wettability. In this study, the surface roughness and contact angle behavior of PLA, PETG, and ABS samples printed via FDM were investigated by varying layer thickness, print orientation, and infill density. The experimental design was created using a Taguchi L16 orthogonal array. Surface roughness was determined by optical profilometry, and wettability was measured by static contact angle tests. Surface topography was supported by scanning electron microscopy (SEM) and three-dimensional surface analyses. The findings revealed that surface roughness is predominantly dependent on layer thickness, whereas wettability is more strongly influenced by printing orientation, which determines the surface’s anisotropy. The developed artificial neural network (ANN) models successfully predicted the trends in surface roughness and contact angle outputs. This study reveals the effect of micro-scale surface structures formed in the FDM process on functional surface behavior, offering a fundamental framework for developing designable surfaces for micromechanical, microfluidic, and biomedical applications. Full article

The study reports on the essential level of details in simulations during the development of structural components if reliability-based design is used to ensure their quality and operational safety. A general method, which is initially introduced, is then applied to an indicator spring of a fuse element during assembly and operation stages. First, it is proven that design of simulations based on orthogonal arrays which includes variations of form, material properties and operating conditions within expected scatter limits provides a comparable determination of the scale parameter for the two-parameter Weibull distribution as the experimental observations of the same process. The shape parameter of the distribution tends to be underestimated by the simulations resulting in a higher scatter of the expected properties than experimentally measured. Next, it is shown that the maximum likelihood method to determine representative parameters of the scatter of assembly and operation stages provides a better match with experimental data than the median rank regression. Finally, a high reliability of the indication has been calculated for the fuse element if both the scatter of the assembly and the operation conditions were considered. Full article

Cold forging backward extrusion is mainly employed in the manufacturing of axisymmetric cup-like components used extensively in automotive and aerospace assemblies due to the process-induced strength that has a pivotal role in such applications. Although cold forging backward extrusion yields mechanically robust components, it demands high forces, subjecting tooling to immense stress, thereby restricting process capacity. The process encounters hindrances in gaining widespread industrial acceptance due to frequent failures of die elements, necessitating proper die design and control of major influencing factors for process viability and cost-effectiveness. The punches in backward extrusion are often susceptible to failures when processing steel billets. The punch service life is significantly affected by geometrical attributes, the type of steel undergoing deformation, and tool manufacturing aspects. Hence, the present study evaluates punch performance in cold forging backward extrusion using optimized geometrical attributes, manufactured through a design of an experimental approach comprising an L9 orthogonal array. The manufacturing factors considered are punch material, hardness, and advanced surface coating. Punches were designed for two industrial components using powder metallurgy (PM) steels—S600, S290, and S590, heat treated to 60–66 HRC, and coated via physical vapor deposition with TiN, AlTiN, and TiAlCN. Punch performance was analyzed against existing industry practices, and the strategy demonstrated improved productivity. Punch performance was determined based on the number of forgings produced before wear- and fatigue-induced failures. Significant improvements in punch performance were witnessed in both high-speed steel (HSS) and PM punches with optimized geometries. Fractographic investigations were carried out on fractured punches and analyzed, focusing on the coating’s effect on the thermal aspects of the punches. The proposed study will assist the cold-forging industry in determining appropriate variables to minimize forming responses, thereby enhancing tool life. The research also benefits industries by enhancing process robustness and improving process efficiency with respect to cost and time. Full article