Search Results (1,603)

This study optimizes the design and fabrication of an injection-molded Alvarez freeform lens using Moldex3D mold flow analysis and CODE V optical design simulations. The dual-software approach facilitates the transition between the manufacturing simulations and the optical design/verification process, thereby addressing the conversion issues between the two analysis modules. The optical quality of the designed lens is evaluated using spot diagram, distortion, and modulation transfer function (MTF) simulations. The Taguchi design methodology is first employed to identify the individual effects of the key injection molding parameters on the quality of the fabricated lens. The quality is then further improved by utilizing two multi-objective optimization methods, namely Gray Relational Analysis (GRA) and Robust Multi-Criteria Optimization (RMCO), to determine the optimal combination of the injection molding parameters. The results demonstrate that RMCO outperforms GRA, showing more substantial improvements in the optical quality of the lens. Overall, the proposed integrated method, incorporating Moldex3D, CODE V, Taguchi robust design, and RMCO analyses, provides an effective approach for optimizing the injection molding of Alvarez freeform lenses, thereby enhancing their quality. Future research could extend this methodology to other optical components and more complex optical systems. Full article

In addressing the challenges posed by the numerous rotor structure parameters and the difficulty in analyzing the air gap magnetic field distribution in interior permanent magnet (IPM) motors, and to enhance the performance of automotive IPM synchronous motors, this paper proposes a multi-objective optimization method based on sensitivity stratification. Firstly, sensitivity analysis is conducted on the positional and shape parameters of the rotor permanent magnets (PMs), and the parameters are stratified according to their sensitivity levels. Subsequently, distinct analysis and optimization methods are applied to parameters of different strata for dual-objective optimization, which aims to increase the amplitude of the air gap flux density and reduce its total harmonic distortion (THD). Moreover, the waveform of the air gap flux density is analyzed to propose a targeted arrangement of magnetic isolation slots, thereby further optimizing the magnetic field distribution. Meanwhile, the demagnetization conditions and influencing factors of the PMs under overload are analyzed to enhance their demagnetization resistance and determine the final structural parameters. Simulation results indicate that, with the application of the proposed optimization method, the fundamental amplitude of the air gap flux density is increased by 0.035 T and THD is decreased by 9.9% when the proposed optimization method is applied. This verifies the effectiveness and feasibility of the method. Full article

A bioresorbable cannulated bone screw was developed using PLA/PCL-based composites reinforced with hydroxyapatite (HA) and titanium dioxide (TiO₂), two additives previously reported to enhance mechanical compliance, biocompatibility, and molding feasibility in biodegradable polymer systems. The design incorporated a crest-trimmed thread and a strategically positioned gate in the thin-wall zone opposite the hexagonal socket to preserve torque-transmitting geometry during micromolding. To investigate shrinkage behavior, a Taguchi orthogonal array was employed to systematically vary micromolding parameters, generating a structured dataset for training a back-propagation neural network (BPNN). Analysis of variance (ANOVA) identified melt temperature as the most influential factor affecting shrinkage quality, defined by a combination of shrinkage rate and dimensional variation. A hybrid AI framework integrating the BPNN with genetic algorithms and particle swarm optimization (GA–PSO) was applied to predict the optimal shrinkage conditions. This is the first use of BPNN–GA–PSO for cannulated bone screw molding, with the shrinkage rate as a targeted output. The AI-predicted solution, interpolated within the Taguchi design space, achieved improved shrinkage quality over all nine experimental groups. Beyond the specific PLA/PCL-based systems studied, the modeling framework—which combines geometry-specific gate design and normalized shrinkage prediction—offers broader applicability to other bioresorbable polymers and hollow implant geometries requiring high-dimensional fidelity. This study integrates composite formulation, geometric design, and data-driven modeling to advance the precision micromolding of biodegradable orthopedic devices. Full article

This study presents the development and optimization of a flexible integrated three-in-one micro-sensor using Micro-Electro-Mechanical Systems (MEMS) technology. To enhance its reliability and performance, the Taguchi Method was employed to analyze and optimize key fabrication parameters, including the electrode area, electrode thickness, and protective layer thickness. An L4 orthogonal array design enabled efficient experimentation with minimal runs. Experimental results demonstrate that optimized parameter combinations significantly improve sensor linearity, sensitivity, and reproducibility. Comparative analysis with commercial sensors shows the superior reliability of the self-fabricated sensor, particularly in airflow velocity detection. The findings validate the use of the Taguchi Method for robust MEMS sensor design and highlight its potential for industrial heating, ventilation, and air conditioning (HVAC) applications. Full article

Lightweight PLA (LW-PLA) filaments enable material-saving designs in fused filament fabrication (FFF), yet optimizing their mechanical performance remains challenging due to temperature-sensitive foaming behavior. This study aims to enhance the structural strength and material efficiency of LW-PLA parts using a multi-objective statistical approach. Four key process parameters—infill density (Id), material flow rate (Mf), wall line count (Wlc), and infill pattern (Ip)—were systematically varied using a Taguchi L₁₆ orthogonal array. Tensile strength (Ts), flexural strength (Fs), and material consumption (Mc) were selected as the critical response metrics. Grey Relational Analysis (GRA) was used to aggregate these responses into a single performance index, and ANOVA determined each factor’s contribution. The optimal combination of 60% infill density, 70% material flow, 4 wall lines, and line infill pattern yielded a 9.02% improvement in the overall performance index compared to the baseline, with corresponding Ts and Fs values of 13.58 MPa and 20.51 MPa. Mf and Wlc were the most influential parameters on mechanical behavior, while Id mainly affected Mc. These findings confirm that integrating Taguchi and GRA enables effective parameter tuning for LW-PLA, balancing strength and efficiency. This work contributes to the development of lightweight, high-performance parts suitable for functional applications such as UAVs and prototyping. Full article

This study explores the mechanical characteristics of 3D-printed specimens fabricated using TPU-95A filament, with a focus on the influence of key printing variables—temperature, speed, and layer height—on tensile strength, toughness, and surface hardness. Through systematic testing, the tensile evaluation revealed a peak tensile strength of 329.02 kgf/cm² and toughness of 1.56 under conditions of elevated temperatures and optimized layer configurations. Similarly, the hardness assessment indicated a maximum average value of 74.9 Shore A, emphasizing the substantial effect of process parameters on material integrity and resilience. A detailed variance analysis confirmed the pivotal roles of temperature and layer height in enhancing mechanical properties. Using a statistical optimization approach, optimal printing conditions were identified, demonstrating that higher temperatures, moderate speeds, and reduced layer heights significantly improve the balance between strength, flexibility, and durability. These findings contribute to the development of tailored fabrication strategies, offering practical insights for applications where precision and mechanical reliability are critical. Full article

Automated optical inspection (AOI) of wood surfaces is critical for ensuring product quality in the furniture and manufacturing industries; however, existing defect detection systems often struggle to generalize across complex grain patterns and diverse defect types. This study proposes a wood defect recognition model employing a Gabor Convolutional Network (GCN) that integrates convolutional neural networks (CNNs) with Gabor filters. To systematically optimize the network’s architecture and improve both detection accuracy and computational efficiency, the Taguchi method is employed to tune key hyperparameters, including convolutional kernel size, filter number, and Gabor parameters (frequency, orientation, and phase offset). Additionally, image tiling and augmentation techniques are employed to effectively increase the training dataset, thereby enhancing the model’s stability and accuracy. Experiments conducted on the MVTec Anomaly Detection dataset (wood category) demonstrate that the Taguchi-optimized GCN achieves an accuracy of 98.92%, outperforming a baseline Taguchi-optimized CNN by 2.73%. Results confirm that Taguchi-optimized GCNs enhance defect detection performance and computational efficiency, making them valuable for smart manufacturing. Full article

Aluminium metal matrix composites (AMMCs) incorporate aluminium alloys reinforced with fibres (continuous/discontinuous), whiskers, or particulate. These materials were engineered as advanced solutions for demanding sectors including construction, aerospace, automotive, and marine. Micro- and nano-scale reinforcing particles typically enable attainment of exceptional combined properties, including reduced density with ultra-high strength, enhanced fatigue strength, superior creep resistance, high specific strength, and specific stiffness. Microstructural, mechanical, and tribological characterizations were performed, evaluating input parameters like reinforcement weight percentage, applied normal load, sliding speed, and sliding distance. Fabricated nanocomposites underwent tribometer testing to quantify abrasive and erosive wear behaviour. Multiple investigations employed the Taguchi technique with regression modelling. Analysis of variance (ANOVA) assessed the influence of varied test constraints. Applied load constituted the most significant factor affecting the physical/statistical attributes of nanocomposites. Sliding velocity critically governed the coefficient of friction (COF), becoming highly significant for minimizing COF and wear loss. In this review, the reinforcement homogeneity, fractural behaviour, and worn surface morphology of AMMCswere examined. Full article

Background/Objectives: Identification of driver gene alterations helps determine first-line treatment for non-squamous non-small cell lung cancer (NSCLC). Precise assessment of tumor cell proportion is critical for accurate detection of gene alterations. ASTRAL was a multicenter, prospective, observational study to investigate the agreement in tumor cell proportion assessments between different raters. Methods: Tissues collected in daily clinical practice from patients with advanced NSCLC were used. Raters included local pathologists, a Central Pathology Committee (CPC), and an artificial intelligence (AI) algorithm. Hematoxylin and eosin-stained slides were assessed by local pathologists, and digitized images of those slides were assessed by the CPC and the AI algorithm. The primary endpoint was agreement in assessment of tumor cell proportion between local pathologists and the CPC, as determined using the intraclass correlation coefficient (ICC). Secondary endpoints included agreement between the AI algorithm and local pathologists or the CPC. Results: Tissue samples from 204 patients were assessed. The ICC for local pathologists vs. the CPC showed poor to moderate agreement (0.588 [95% confidence interval (CI) 0.483–0.674]). The AI algorithm showed moderate agreement with the CPC (ICC 0.652 [95% CI 0.548–0.733]), and poor to moderate agreement with local pathologists (ICC 0.465 [95% CI 0.279–0.604]). Conclusions: The ICC for the AI algorithm vs. the CPC was numerically highest among the rater pairs, indicating a level of usefulness for the algorithm. Continued efforts are needed to ensure the accurate estimation of tumor cell proportion. Integration of AI algorithms in real-world practice may contribute to this. Full article

Burrowing mammals function as ecosystem engineers by creating spatial heterogeneity in the soil structure and vegetation composition, thereby providing microhabitats for a wide range of organisms. These keystone species play a crucial role in maintaining local ecosystem functions and delivering ecosystem services. However, in Mongolia, where overgrazing has accelerated due to the expansion of a market-based economy, scientific knowledge remains limited regarding the impacts of human activities on such species. In this study, we focused on the Siberian marmot (Marmota sibirica), an ecosystem engineer inhabiting typical Mongolian steppe ecosystems. We assessed the relationship between the spatial distribution of marmot burrows and vegetation conditions both inside and outside Hustai National Park. Burrow locations were recorded in the field, and the Normalized Difference Vegetation Index (NDVI) was calculated, using Planet Lab, Dove-2 satellite imagery (3 m spatial resolution). Through a combination of remote sensing analyses and vegetation surveys, we examined how the presence or absence of anthropogenic disturbance (i.e., livestock grazing) affects the ecological functions of marmots. Our results showed that the distance between active burrows was significantly shorter inside the park (t = −2.68, p = 0.0087), indicating a higher population density. Furthermore, a statistical approach, using beta regression, revealed a significant interaction between the burrow type (active, non-active, off-colony area) and region (inside vs. outside the park) on the NDVI (e.g., outside × non-active: z = −5.229, p < 0.001). Notably, in areas with high grazing pressure outside the park, the variance in the NDVI varied significantly as a function of burrow presence or absence (e.g., July 2023, active vs. off-colony area: F = 133.46, p < 0.001). Combined with vegetation structure data from field surveys, our findings suggest that marmot burrowing activity may contribute to the enhancement of vegetation quality and spatial heterogeneity. These results indicate that the Siberian marmot remains an important component in supporting the diversity and stability of steppe ecosystems, even under intensive grazing pressure. The conservation of this species may thus provide a promising strategy for utilizing native ecosystem engineers in sustainable land-use management. Full article

This study reports the separation of methyl ethyl ketone (MEK), a relevant compound in the biorefinery context, from aqueous solutions using activated carbons derived from avocado seed biomass. Two synthesis routes were explored via chemical and thermal activation with H₂SO₄ and KOH. A Taguchi experimental design was applied to tailor synthesis conditions, with MEK adsorption capacity as the target property. Adsorption kinetics and isotherms were evaluated to determine the thermodynamic behavior of MEK separation using the best-performing activated carbons. The carbon activated with H₂SO₄ achieved the highest adsorption capacity (142 mg g⁻¹) at 20 °C and pH 4, surpassing KOH-based materials. This enhanced performance correlated to increased surface area and acidic oxygenated functionalities. However, higher pH and temperature reduced the adsorption efficiency for all adsorbents. Comprehensive characterization was performed using XRD, XRF, FTIR, SEM, N₂ adsorption–desorption isotherms, pH at point of zero charge, and surface acidity/basicity analysis via Boehm titration. Thermodynamic data and surface characterization indicated that MEK adsorption occurs via a double-layer mechanism dominated by electrostatic interactions and hydrogen bonding. The findings highlight an optimized approach for tailoring avocado-based activated carbons to efficiently recover MEK from aqueous media, supporting its potential application in downstream purification of fermentation broths for biofuel production and energy transition processes. Full article

Cocoa represents a crucial source of income in coastal regions of Ecuador, where the product is exported for the production of high-value chocolates. However, elevated levels of cadmium (Cd) in cocoa beans, attributable to volcanic soils, have the potential to impede international trade, particularly in accordance with European Union regulations. The main objective of this study was to reduce Cd concentrations in cocoa beans of two genotypes, Nacional and CCN-51, by applying different doses of pectin trans-eliminase (PTE) enzyme during the fermentation process in conjunction with mucilage washing techniques, pre-drying resting periods, and various drying methods. To this end, a Taguchi orthogonal design (L9) was employed to evaluate nine treatments per genotype, complemented with two controls. The most efficacious treatment for Nacional was identified as T7, involving a 0.30 mL·kg⁻¹ PTE dose, the absence of mucilage washing, a 48 h resting period, and drying in a marquee. This treatment resulted in a 68.6% reduction in Cd concentration (from 0.28 to 0.09 mg·kg⁻¹). For CCN-51, T3 (0.10 mL·kg⁻¹ PTE, complete washing, 48 h resting, and splint drying) yielded a 26.4% reduction in Cd (from 0.42 to 0.31 mg·kg⁻¹). It is noteworthy that none of the treatments exceeded the EU regulatory threshold of 0.8 mg·kg⁻¹. A physico-chemical analysis was conducted, which revealed significant treatment effects on pH (ranging from 5.63 to 6.85) and acidity (0.02% to 0.03%). Sensory evaluation indicated enhancements in cocoa and nutty flavors, along with a reduction in undesirable astringency and bitterness, particularly in Nacional samples. The findings of this study demonstrate that the combination of enzyme-assisted fermentation and optimized postharvest techniques represents a pragmatic approach to the mitigation of cadmium in cocoa, while simultaneously preserving or enhancing product quality. Full article

This study addresses the use of porcelain polishing waste as a pyro-expansive agent in clay-based formulations for the production of lightweight aggregates, aiming to reduce the consumption of natural resources and mitigate environmental impacts. In line with circular economy principles and sustainable construction goals, this study investigates the potential use of porcelain polishing waste as a pyro-expansive agent in clay-based formulations for producing sustainable lightweight aggregates. Using the Taguchi method and ANOVA, the effects of key processing parameters were evaluated. The results demonstrated a broad range of volumetric changes, from shrinkage of 40.84% to expansion of 91.69%, depending on the formulation and processing conditions. The aggregates exhibited specific mass values ranging from 0.99 g/cm³ to 2.36 g/cm³, water absorption up to 3.29%, and mechanical strength from 4.57 MPa to 39.87 MPa. Notably, nine of the sixteen experimental conditions met the technical standards for classification as LWA, indicating suitability for applications in high-strength, structural, and non-structural lightweight concretes, as well as lightweight mortars. The performance of these materials was directly linked to the chemical and mineralogical characteristics of the precursors and the proportion of pyro-expansive waste used. Overall, the findings suggest that 50% of the produced aggregates are viable for high-performance concrete applications, offering an environmentally responsible alternative to virgin raw materials and contributing to sustainable waste valorization in the ceramic and construction industries. Full article

Laser-Directed Energy Deposition (L-DED) is an additive manufacturing technique used for producing and repairing components, mainly for coating applications, depositing metal matrix composites such as cemented carbides, composed of hard metal carbides and a metallic binder. In this sense, this study evaluated the preparation of a ready-to-press WC-25Co powder as a reliable feedstock for L-DED process. This powder required pre-heat treatment studies to prevent fragmentation during powder feeding, due to the absence of metallurgical bonding between WC and Co particles. In the current study, the Taguchi methodology was used, varying laser power, powder feed rate, and scanning speed to reach an optimised deposition window. The best bead morphology resulted from 2400 W laser power, 11 mm/s scanning speed, and 9 g/min feed rate. Moreover, defects such as porosity and cracking were mitigated by applying a remelting strategy of 2400 W and 9 mm/s. Therefore, a perfect deposition is obtained using the optimised processing parameters. Microstructural analysis of the optimised deposited line revealed a fine structure, comprising columnar and equiaxed dendrites of complex carbides. The average hardness of the deposited WC-25Co powder on a AISI 1045 steel was 854 ± 37 HV0.2. These results demonstrate the potential of L-DED for processing high-performance cemented carbide coatings. Full article

Silicon carbide (SiC) is widely utilized in semiconductors, microelectronics, optoelectronics, and other advanced technologies. However, its inherent characteristics, such as its hardness, brittleness, and high chemical stability, limit the processing efficiency and application of SiC wafers. This study explores the use of plasma etching as a pre-treatment step before chemical mechanical polishing (CMP) to enhance the material removal rate and improve CMP efficiency. Experiments were designed based on the Taguchi method to investigate the etching rate of plasma under various processing parameters, including applied power, nozzle-to-substrate distance, and etching time. The experimental results indicate that the etching rate is directly proportional to the applied power and increases with nozzle-to-substrate distance within 3–5 mm, while it is independent of etching time. A maximum etching rate of 5.99 μm/min is achieved under optimal conditions. And the etching mechanism and microstructural changes in SiC during plasma etching were analyzed using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), white light interferometry, and ultra-depth-of-field microscopy. XPS confirmed the formation of a softened SiO₂ layer, which reduces hardness and enhances CMP efficiency; SEM revealed that etching pits form in relation to distance; and white light interferometry demonstrated that etching causes a smooth surface to become rough. Additionally, surface defects resulting from the etching process were analyzed to reveal the underlying reaction mechanism. Full article