Search Results (1,289)

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

During the actual service process, water-lubricated bearings on ships are often in complex operating environments such as low speed, heavy load and salt-sand water areas. To meet the requirements of high load-bearing capacity, long service life and the ability to discharge sand and dissipate heat during the service of bearings, research has been conducted on water-lubricated bearings made of polyetheretherketone (PEEK) with a semi-groove structure. Mathematical and physical models based on the averaged Reynolds equation have been established. By adopting the method of multi-physics field coupling, the lubrication characteristics of the bearings under the coupling influence of multiple factors in the salt-sand water environment (lubrication interface (the surface roughness of the bearing bush), different working conditions (water supply pressure, rotational speed, eccentricity)) are analyzed. Finally, a water-lubricated bearing test bench is set up to conduct bearing lubrication performance tests under multiple factors. The research shows that compared with liquid water, the salt-sand water environment exhibits better lubrication characteristics. The maximum water film pressure, the deformation amount of the bearing bush and the bearing capacity of the bearings increase with the increase of the rotational speed, water supply pressure and eccentricity, while the friction coefficient decreases. With the increase of the roughness of the bearing bush, these parameters decrease slightly and the friction coefficient increases. The presence of salt-sand particles can weaken the influence of roughness on the lubrication characteristics of the bearings. After considering the thermal effect, the mechanical load and thermal load act on the surface of the bearing bush together, resulting in an increase in the deformation amount of the bearing bush, a 0.11% drop in the water film pressure, and the highest temperature of the water film being concentrated at the outlet of the groove. The local semi-groove structure of PEEK can make the friction coefficient as low as 0.019. The comparison errors between the simulation and the experiment are within 10% (for water film pressure) and 2.6% (for friction coefficient), which verifies the reliability of the model. Full article

After rough planing, defects such as wormholes and small patches of green bark residue and decay are often overlooked and misclassified. Strip-like defects, including splinters and chipped edges, are easily confused with the natural bamboo grain, and a single elongated defect is frequently fragmented into multiple detection boxes. This study proposes a modified TASNet-YOLO model, an improved detector built on YOLO11n. Unlike prior YOLO-based bamboo defect detectors, TASNet-YOLO is a mechanism-guided redesign that jointly targets two persistent failure modes—limited visibility of small, low-contrast defects and fragmentation of elongated defects—while remaining feasible for real-time production settings. In the backbone, a newly designed TriMAD_Conv module is introduced as the core unit, enhancing the detection of wormholes as well as small-area defects such as green bark residue and decay. The additive-gated C3k2_AddCGLU is further integrated at selected C3k2 stages. The combination of additive interaction and CGLU improves channel selection and detail retention, highlighting differences between splinters and chipped edges and bamboo grain strips, thereby reducing false positives and improving precision. In the neck, the neck replaces nearest-neighbor upsampling and CBS with SNI-GSNeck to improve cross-scale alignment and fusion. Under an acceptable real-time budget, predictions for splinters and chipped edges become more contiguous and better aligned to edges, while wormholes predictions are more circular and less noisy. Experiments on our in-house dataset (8445 bamboo-strip defect images) show that, compared with YOLO11n, the proposed model improves detection accuracy by 5.1%, achieves 106.4 FPS, and reduces computational costs by 0.4 GFLOPs per forward pass. These properties meet the throughput demand of 2 m/s conveyor lines, and the compact model size and compute footprint make edge deployment straightforward for fast online screening and preliminary quality grading in industrial production. Full article

The turbine mortise is a critical structural feature of turbine disks, and its manufacturing quality directly determines the performance and service life of aircraft engines. With the increasing application of advanced nickel-based superalloys, severe tool wear in conventional mechanical broaching of turbine mortises has emerged as a key limitation, substantially elevating production costs. Electrochemical broaching (ECB), which removes material through anodic dissolution reactions, eliminates tool wear and thus offers low cost and efficiency advantages, making it a promising method for turbine mortise fabrication. In this study, COMSOL Multiphysics 6.2 was employed to simulate the multiphysics field comprising the electric field, flow field, temperature field, bubble ratio, and dynamic mesh and elucidate the evolution of the electric field during the ECB process. ECB experiments of specimens on Inconel 718 were conducted under different feed speeds. On this basis, optimal processing parameters were identified. The results of the mid-position ECB experiments revealed five distinct dissolution states: pre-processing, pre-transition, stable dissolution, post-transition, and post-processing stages. A material dissolution mechanism model for the ECB process was established. Finally, fir-tree turbine mortises were successfully manufactured on Inconel 718 using a self-developed specialized electrochemical machining system at a feed speed of 70 mm/min. The mortise profile demonstrated dimensional deviations of (+16 to −21) μm, with working surface variations maintained within ±5 μm. The machined surfaces exhibited uniform and dense morphology with a surface roughness of Ra 0.275 μm. Three sets of mortise specimens processed under identical parameters showed excellent consistency, presenting a maximum deviation in profile removal thickness of +4.1 μm. The tool cathode was repeatedly reused without any detectable wear. Full article

Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored for flood risk management, many existing studies remain limited to one-dimensional (1D) models or use coarse-resolution terrain data, often underestimating flood risk and failing to produce critical multivariate flood characteristics in densely built urban areas. This study applies a two-dimensional (2D) hydraulic modeling framework in HEC-RAS combined with GIS-based spatial analysis, using a high-resolution (1 × 1 m) LiDAR-derived Digital Terrain Model (DTM) and a hybrid mesh refined between 2 × 2 m and 8 × 8 m, with the main contributions represented by the specific application context and methodological choices. A key methodological aspect is the direct integration of synthetic hydrographs with defined exceedance probabilities (10%, 1%, and 0.1%) into the 2D model, thereby reducing the need for extensive hydrological simulations and defining a data-driven approach for resource-constrained environments. The primary novelty is the application of this high-resolution urban modeling framework to a Romanian urban–peri-urban setting, where detailed hydrological observations are scarce. Unlike previous studies in Romania, this approach applies detailed channel and floodplain discretization at high spatial resolution, explicitly incorporating anthropogenic features like buildings and detailed land use roughness for the accurate representation of local hydraulic dynamics. The resulting outputs (inundation extents, depths, and velocities) support risk assessment and spatial planning in the Ungheni locality (Iași County, Romania), providing a practical, transferable workflow adapted to data-scarce regions. Scenario results quantify vulnerability: for the 0.1% exceedance probability scenario (with a calibration accuracy of ±15–30 min deviation for peak flow timing), the flood risk may affect 882 buildings, 42 land parcels, and 13.5 km of infrastructure. This framework contributes to evidence-based decision-making for climate adaptation and disaster risk reduction strategies, improving urban resilience. Full article

Objective: The purpose of this study was to compare the flexural strength, flexural modulus, hardness, and surface roughness of one brand each of 3D-printed and heat-cured acrylic resin materials after they were immersed in various disinfection solutions. Methods: The study included 160 specimens, consisting of 80 heat-cured and 80 3D-printed specimens. Forty specimens of each resin material type were prepared for flexural testing, while an additional forty specimens were designated for hardness and surface roughness assessments. Each collection of 40 specimens was subsequently randomized into four subgroups (n = 10) for immersion in either distilled water (control), 1% sodium hypochlorite, Superdent, or Kin Oro denture cleansers. Flexural test, hardness, and surface roughness assessments were performed. Data analysis was conducted using SPSS, with a level of significance set at p < 0.05. Results: Flexural strength and surface roughness did not differ significantly between the two resin types. Flexural modulus was significantly higher in the heat-cured resin among all the disinfectants (p = 0.000). The heat-cured resin had significantly higher microhardness than the 3D-printed resin among the disinfectants except for the Kin Oro group, and both resins showed a significant reduction in hardness after immersion in disinfectants compared to distilled water (p < 0.05). Conclusions: The heat-cured resin demonstrated higher flexural modulus and surface hardness compared to the 3D-printed resin. Flexural strength and surface roughness were comparable between the two materials. Both resins had their highest mechanical properties in distilled water. Full article

The increasing reliance on conventional coatings such as WC-Co raises serious environmental and health concerns due to the toxicity of cobalt and the ecological footprint of these materials. To address this challenge, the present study explores the development of eco-friendly multifunctional coatings via the Plasma Spray (PS) process, using titanium (Ti), silicon carbide (SiC), and tungsten carbide-cobalt (WC-Co) mixtures as alternative feedstocks. Steel substrates were coated under different deposition strategies (powder mixing, layer-by-layer) and current settings (800-900 A). The coatings were characterized by scanning electron microscopy (SEM/EDX), 3D profilometry, sliding wear testing, and potentiodynamic corrosion measurements. Results showed that Ti-WC (mix, 900 A) and Ti-SiC (layer, 900 A) coatings achieved the most favorable performance, combining excellent adhesion, uniform coverage, reduced porosity, and improved resistance to wear and corrosion compared to conventional Cr₂O₃ coatings. Notably, Ti-WC coatings provided surface roughness values comparable to Cr₂O₃, while significantly lowering the environmental impact. These findings demonstrate that PS-based Ti-WC and Ti-SiC systems can serve as sustainable and high-performance alternatives for protective applications in harsh environments, particularly in marine industries, supporting the transition toward coatings with reduced ecological footprint. Full article

Pipelines and power cables are critical infrastructures in coastal areas for transporting energy resources from offshore renewable installations to onshore grids. It is important to investigate the hydrodynamic forces on pipelines and cables and their surrounding flow fields, which are highly related to their on-bottom stability. The time-varying hydrodynamic forces coefficients and unsteady surrounding flows of a near-seabed pipeline subjected to a wave-induced oscillatory boundary layer flow are studied through numerical simulations. The Keulegan–Carpenter numbers of the oscillatory flow are up to 400, which are defined based on the maximum undisturbed near-bed orbital velocity, the pipeline diameter and the period of the oscillatory flow. The investigated Reynolds number is set to $1\times {10}^4$, defined based on $U_w$ and $D$. The influences of different seabed roughness ratios $k_s/D$ (where $k_s$ is the Nikuradse equivalent sand roughness) up to 0.1 on the hydrodynamic forces and the flow fields are considered. Both a wall-mounted pipeline with no gap ratio to the bottom wall and a pipeline with different gap ratios to the wall are investigated. The correlations between the hydrodynamic forces and the surrounding flow patterns at different time steps during one wave cylinder are analyzed by using the force partitioning method and are discussed in detail. It is found that there are influences of the increasing $k_s/D$ on the force coefficients at large $\mathrm{K}\mathrm{C},$ while for the small $\mathrm{K}\mathrm{C}$, the inertial effect from the oscillatory flow dominates the force coefficients with small influences from different $k_s/D$. The FPM analysis shows that the elongated shear layers from the top of the cylinder contribute to the peak values of the drag force coefficients. Full article

Inconel 625 is a nickel-based superalloy widely applied in aerospace and energy sectors due to its high strength and corrosion resistance. However, its poor machinability remains a significant challenge in precision manufacturing. This study investigates the influence of tool geometry and cutting parameters on surface roughness of Inconel 625 during turning operations under the minimum quantity lubrication (MQL) conditions. Experiments were carried out using three types of cutting inserts with distinct chip breaker geometries while systematically varying the cutting speed, feed rate, and depth of cut. The results were statistically analyzed using analysis of variance (ANOVA) to determine the significance of individual factors. The findings reveal that both the type of cutting insert and the process parameters have a considerable effect on surface roughness, which is the key output examined in this study. Cutting forces and chip type were examined to provide complementary insights and improve understanding of the observed relationships. Based on the results, an optimal set of cutting data was proposed to achieve a required surface roughness during the turning of Inconel 625 with MQL. Furthermore, a practical algorithm was developed to support the selection of cutting parameters in industrial applications. Analysis of the results showed that a cutting insert with a 0.4 mm corner radius achieved the required surface finish (Rz ≤ 0.4 µm). Furthermore, the analysis revealed a significant effect of the thermal properties of Inconel 625 on machining results and chip geometry. Full article

This study addresses the challenges in machining the raceways and oil holes of aircraft engine bearing rings by conducting abrasive flow machining experiments on main bearing rings which had undergone ultra-precision grinding. Viscoelastic abrasive media containing cubic boron nitride of different particle sizes is used during the experiments. The results show that bearing performance is improved significantly in terms of surface roughness and residual compressive stress consequently; the overall surface quality is raised. The machining process meets the precision requirements for the main bearings of this type of aircraft engine, validating the feasibility and effectiveness of Abrasive Flow Machining (AFM), and the foundation for further optimization of this process is set through this research. Full article

Accurate prediction of soil–structure interface shear strength (τ_max) is critical for reliable geotechnical design. This study combines experimental testing with interpretable machine learning to overcome the limitations of traditional empirical models and black-box approaches. Ninety large-displacement ring shear tests were performed on five sands and three interface materials (steel, PVC, and stone) under normal stresses of 25–100 kPa. The results showed that particle morphology, quantified by the regularity index (RI), and surface roughness (R_t) are dominant factors. Irregular grains and rougher interfaces mobilised higher τ_max through enhanced interlocking, while smoother particles reduced this benefit. Harder surfaces resisted asperity crushing and maintained higher shear strength, whereas softer materials such as PVC showed localised deformation and lower resistance. These experimental findings formed the basis for a hybrid symbolic regression framework integrating Genetic Programming (GP) with Shapley Additive Explanations (SHAP), Fourier feature augmentation, and physics-informed constraints. Compared with multiple linear regression and other hybrid GP variants, the Physics-Informed Neural Fourier GP (PIN-FGP) model achieved the best performance (R² = 0.9866, RMSE = 2.0 kPa). The outcome is a set of five interpretable and physics-consistent formulas linking measurable soil and interface properties to τ_max. The study provides both new experimental insights and transparent predictive tools, supporting safer and more defensible geotechnical design and analysis. Full article

Magnesium alloys, like AZ31, possess a desirable low weight and high specific strength, which make them favorable for aerospace and auto applications, yet their difficulty to machine limits their broader implementation for the industry. Electrical discharge machining (EDM) is an effective technology for machining difficult-to-machine materials, particularly when the materials are reinforced with ceramic and graphene-based fillers. This study examines the impact of reinforcement percentage (R) and different electrical discharge machining (EDM) parameters such as current (I), pulse on time (T_on) and pulse off time (T_off) on the material removal rate (MRR) and surface roughness (SR) of AZ31/B₄C/GNPs composites. The combined reinforcement range varies from 2 wt.% to 4 wt.%. The Taguchi design (L27) is utilized to conduct the experiments in this study. ANOVA of the experimental data indicated that current (I) significantly affects MRR and SR, exhibiting the greatest contribution of 44.93% and 51.39% on MRR and SR, respectively, among the variables analyzed. The surface integrity properties of EDMed surfaces are examined using SEM under both higher and lower material removal rate settings. Diverse machine learning techniques, including linear regression (LR), polynomial regression (PR), Random Forest (RF), and Gradient Boost Regression (GBR), are employed to construct an efficient predictive model for outcome estimation. The built models are trained and evaluated using 80% and 20% of the total data points, respectively. Statistical measures (MSE, RMSE, and R²) are utilized to evaluate the performance of the models. Among all the developed models, GBR exhibited superior performance in predicting MRR and SR, achieving high accuracy (exceeding 92%) and lower error rates compared to the other models evaluated in this work. This work demonstrated the synergy between techniques in optimizing EDM performance for hybrid composites using a statistical design and machine learning strategies that will facilitate greater use of hybrid composites in high-precision engineering applications and advanced manufacturing sectors. Full article

This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595°, surface roughness of 8.9897 µm, and material removal rate of 138.13 g/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates. Full article

In order to approximate an undefinable set of objects by using the extensions in OE-concept lattices, this study combines three-way concept analysis with covering-based rough set and introduces an innovative approach for managing uncertain information and decision-making. This approach employs the minimal neighborhood of the maximal description, which is determined by meet-irreducible elements, to define the lower and upper of an undefinable set. On this basis, we formalize the concepts of lower and upper approximation OE-concepts and propose a three-way approximation optimization algorithm. Experimental results demonstrate the effectiveness and efficiency of our algorithm. Full article

This paper focuses on the scoring-wheel cutting process for circular structures of OLED display panels, conducting in-depth research through an experiment–analysis–optimization system. Based on the Taguchi experimental design, a three-factor, five-level experiment is conducted, with the blade wheel angle (A), cutting speed (B), and pressure (C) set as influencing factors, and the scratch depth (h), width (w), median crack depth (l), and transverse crack width (d) set as evaluation indicators. The experiments are carried out using a self-developed dicing-wheel cutting device, and the morphology, roughness, and hardness of the cutting surface and cross-section are characterized by means of ultra-depth-of-field microscopy, laser confocal microscopy, microhardness tester, and other equipment. The research shows that the order of factors affecting the cutting quality is as follows: A > C > B. Through the analysis of morphology and crack characteristics, it is determined that the optimal parameter combination is a dicing wheel angle of 130°, a cutting speed of 20 mm/s, and a pressure of 11 N. The verification results indicate that this combination can reduce surface roughness, stabilize hardness, and realize efficient and precise processing of special-shaped structures in OLED display panels, providing strong theoretical and technical support for industrial process optimization. Full article