Search Results (88)

To address the limited design flexibility in meshing equations, complex modeling processes, and a lack of systematic research on bidirectional transmission in traditional parallel-axis line gears, this paper proposes a rapid design method for bidirectional transmission parallel-axis external line gears (BTPELG). Firstly, a multi-coordinate system is established, and homogeneous transformation matrices are derived. Secondly, the meshing equation is extended with an angular parameter to obtain a widely applicable conjugate condition. Then, two pairs of conjugate contact curves and center guide curves are constructed, and the offset vector for a circular-arc tooth profile is derived. Subsequently, taking the equidistant cylindrical helix as the driving contact curve, a 3D solid model is built via the sweeping method. Finally, kinematic simulations and experiments on 3D-printed prototypes verify the transmission ratios under forward and reverse conditions. Results show stable bidirectional transmission with average ratios matching theory and small fluctuations, confirming the method’s feasibility and providing a reference for rapid design and engineering applications. Full article

Cold bending provides a cost-effective method for fabricating triangular glass units for free-form architectural envelopes. Replacing conventional continuous edge constraints with discrete point clamps reduces over-constraint but introduces pronounced bending–membrane coupling in the unsupported spans between adjacent clamps. Consequently, the mechanisms governing local instability and optical-quality degradation remain insufficiently understood. In this study, cold-bending tests were performed on isosceles triangular fully toughened glass plates to measure out-of-plane deflection and surface-strain evolution. The experimental data were then used to establish and validate an Abaqus finite element model for systematic parametric analysis. Based on von Kármán’s large-deflection theory, a semi-empirical reduced-order framework that combines modal superposition with the response-surface method was developed to identify instability-sensitive configurations. The results show that, under weak constraints and large vertex angles, the panel response changes from a bending-dominated regime to a strongly nonlinear large-deflection regime governed by membrane effects; this transition is marked by a reversal of mid-span deflection and a compressive-to-tensile stress transition. Increasing the number of clamps from two to four substantially suppresses both global and local distortion by shortening the free spans and redistributing membrane strain energy, reducing peak mid-span deflection by 47–68%, and satisfying the EN 12150-1 limits for both bow deformation and local distortion. The height-to-base ratio is the dominant geometric parameter controlling instability. Under two-point support, a critical response turning point occurs at a height–base ratio of approximately 0.5 before the material fracture limit is reached, defining a geometric boundary below which optical serviceability failure accelerates. These findings provide a theoretical basis and quantitative engineering guidance for optimizing the cold-bending process of isosceles triangular fully toughened glass plates. Full article

To address the defects of the traditional egret swarm optimization algorithm (ESOA) in high-dimensional complex optimization problems, such as low optimization accuracy, weak ability to escape from local extrema, rapid decay of population diversity, and insufficient efficiency in the late convergence stage, an improved egret swarm optimization algorithm (IESOA) combining variable-factor weighted learning and adjacent generation dimension crossover strategy is proposed. Firstly, a dynamic change rule of core model parameters (exploration factor ω and exploitation factor μ) is constructed to adaptively adjust with the iteration process, so as to balance global exploration and local exploitation capabilities. Secondly, a multi-individual variable-factor weighted learning mechanism is designed to enable offspring individuals to inherit the position information of following individuals, sub-population optimal individuals, and global optimal individuals simultaneously, avoiding excessively fast assimilation of the population. Furthermore, an adjacent generation dimension crossover strategy is established to update the optimal individual based on the priority principle of absolute dimension difference, fully retaining the historical optimal dimension information. Finally, a preferred mutation reverse learning strategy is integrated to further enhance the local extremum escape ability and convergence accuracy of the algorithm. The IESOA is compared with eight algorithms, including PSO, DE, SBOA, BKA, HHO, DOA, and the original ESOA on CEC2014 and CEC2019 benchmark test suites. The results show that IESOA presents significant advantages in optimization accuracy, convergence speed, and stability. The algorithm is applied to three typical engineering optimization problems: reinforced concrete beam design, welded beam design, and pressure vessel design, which effectively reduces the structural design cost and verifies its application value in practical engineering. Full article

Large-scale shell-and-tube heat exchangers operate for extended periods, critically affecting the energy efficiency and safety of hydrogen production processes. However, online condition monitoring on industrial distributed control systems (DCSs) is often hindered by an engineering trilemma: high-fidelity mechanistic models incur prohibitive computational latency; static constant-parameter models suffer from severe systematic bias; and purely data-driven models risk yielding non-physical predictions under out-of-distribution scenarios such as variable-load operations. To address these challenges, this study proposes a physics-guided adaptive digital twin tailored to high-noise industrial DCS environments. Energy conservation and the counterflow logarithmic mean temperature difference (LMTD) relation are embedded as hard constraints in a lightweight reduced-order model (ROM). On this basis, a closed-loop online adaptation strategy—comprising physical-bound checking, window-wise inverse estimation, anomaly rollback, and exponentially weighted moving average (EWMA) smoothing—treats the overall heat transfer coefficient U as an equivalent time-varying parameter that co-evolves with operating regimes. Validation on real plant DCS data under variable-load conditions shows that, compared with a conservative fixed-U baseline, the proposed online update eliminates massive systematic overestimations (up to tens of degrees Celsius) and suppresses inversion oscillations caused by small cold-side temperature differences and sensor noise. Relative to an overfitting-prone data-driven baseline, the framework retains millisecond-level inference latency while enforcing thermodynamic feasibility, thereby establishing a dynamic healthy baseline. This baseline provides a proxy indicator for distinguishing load-induced reversible variations from potential degradation-related residual trends. Full article

The black-winged kite algorithm (BKA) integrates the Cauchy mutation strategy and the leader selection strategy to simulate high-altitude circling exploration, fixed-point diving attack, and group cooperative migration of the black-winged kites to approximate the global optimal solution. The BKA exhibits deficiencies in ponderous convergence efficacy, inefficient calculation precision, and insufficient population diversity. To strengthen the convergence property and computational practicability, an enhanced BKA with multiple strategies (MSBKA) is advocated to accommodate global optimization and constrained engineering applications. The objective is to systematically verify its advancement and competitiveness and accurately actualize the global optimal solution. The ranking-based differential mutation can strengthen population information interaction, accelerate convergence efficiency, restrain premature convergence, diminish homogenization competition, promote exploration and exploitation, intensify elite individual guidance, downscale ineffective iterations, and materialize orderly population renewal. The simplex method can execute the local refinement operations of reflection, expansion, compression and contraction, strengthen local mining efficiency, ameliorate solution accuracy, abate parameter sensitivity, eschew local optimal traps, accelerate accurate convergence, and preserve the optimal individual potential. The elite opposition-based learning strategy can fabricate reverse solutions, expand the monolithic detection space, shorten the convergence process, elevate the quality of initial and iterative solutions, boost population diversity, guide intelligent search direction, and relieve premature convergence. The MSBKA utilizes deficiency orientation, strategy adaptation, and collaborative search to accomplish the realistic demands of high-precision, high-efficiency and strong constraint adaptation, surmount the static trade-off dilemma, endow a strong directional abscond mechanism to replace random perturbation, and actualize the inertia of directional exploration and the blind spots of solution exploitation. Twenty-three benchmark functions and six real-world engineering designs are employed to authenticate theoretical superiority and engineering practicability. The experimental results demonstrate that the MSBKA incorporates strong practicability and reliability to strengthen information interaction, restrain search stagnation, diminish convergence oscillation and fluctuation, facilitate globalized discovery and localized extraction, expedite convergence efficacy, ameliorate solution precision, and consolidate stability and robustness. Full article

Fused Deposition Modeling (FDM) components require accurate identification of printing parameters to ensure reliable quality assessment and support scalable reverse-engineering workflows. The objective of this study is to evaluate whether mechanical response curves obtained from tensile tests can be used to infer key manufacturing parameters, specifically part orientation, layer thickness, and infill density. Force–displacement and stress–strain data were transformed into image-based representations and classified using several individual and ensemble machine learning models. In addition, the influence of applying a moving-average filter to smooth the curve-derived images was analyzed. Ensemble methods, particularly the AdaBoost classifier, achieved the best performance across the evaluated variables, with the highest accuracy obtained from unfiltered stress–strain images. Under limited-data conditions, ensemble models consistently outperformed individual classifiers, whereas Multilayer Perceptron and Support Vector Machine models exhibited more stable but lower predictive accuracy. These results demonstrate that mechanical response curves contain relevant information about manufacturing conditions and can be used to infer FDM printing parameters. The proposed approach offers a potential non-destructive framework for parameter identification in additively manufactured components, thereby improving traceability and quality control in additive manufacturing processes. Full article

This study proposes a useful multi-criteria optimization approach for defining the proper fabrication factors for the CNC milling process on the inclined surfaces of SKD 11 material. The method is to be used in mold fabrication technology within the field of mechanical engineering. A combination technique of the Taguchi technique (TM), response surface method (RSM), and Lichtenberg optimization algorithm (LA) was proposed to optimize the fabrication factors for enriching the superiority attributes. In the first stage, several initial experiments of the fabricating parameters were generated by the TM. Secondly, the mathematical equations among the main fabricating parameters, the surface roughness, the flatness, and the CNC milling time were then established by the RSM. Significant influences of fabrication elements on surface roughness, flatness, and CNC milling time were evaluated by variance analysis and sensitivity analysis based on three distinct CNC milling toolpath strategies. Finally, the Lichtenberg optimization algorithm was carried out based on regression equations to define the optimized factors for three cutting strategies. The optimized results showed that the reverse CNC milling toolpath strategy was the best for achieving the three quality responses. Furthermore, the results demonstrated that the inaccuracies among optimized as well as experiment confirmations for the surface roughness, flatness and CNC milling time were 6.54%, 18.182% and 11.972%, respectively. The verifications of experiment results were relatively suitable with the anticipated consequences. The outcomes reveal that an integration optimization methodology is a successful approach to tackling the multi-objective optimal problem of determining the best CNC milling parameters for the cartwheel specimen made of SKD11 material in injection mold technology. It can also be expanded to apply to complicated multi-criteria optimization problems. Full article

Extracting standardized digital design patterns from real knitted fabric images is critical for textile reverse engineering and digital archiving. Unlike smooth graphics, knitted fabrics exhibit high-frequency textures from yarn loop interlacing, introducing significant grayscale variations within same-color regions. Existing algorithms struggle to distinguish these from pattern edges, causing color quantization and segmentation failures. To suppress yarn texture while preserving edges between color blocks, we propose an adaptive pattern extraction method using Bayesian-optimized bilateral filtering. The primary contribution lies in providing a domain-specific, application-focused integrated framework. Specifically, (1) a knitting-texture-aware multidimensional evaluation parameter is constructed by integrating physical-cause-based texture features (gray-level co-occurrence matrix (GLCM) contrast, homogeneity, and Laplacian variance) with perception-based edge preservation metrics (the Sobel operator and the structural similarity index (SSIM)), enabling accurate discrimination between yarn-level texture noise and pattern-level color block boundaries—a distinction that generic image quality metrics cannot make. (2) Then, this domain-specific objective function is embedded within a Bayesian optimization framework to achieve automatic, zero-shot, per-image parameter adaptation across different knitting processes, without requiring any external training data. K-means color quantization maps in continuous tones to discrete classes, generating standardized patterns meeting knitting requirements. Experiments on 316 samples covering six processes show our method outperforms standard denoising and advanced algorithms like relative total variation (RTV), achieving an average SSIM of 0.83 and PSNR of 26.92 dB, reducing processing time from 15–30 min to 21 s per image, providing efficient automation for knitted Computer-Aided Design (CAD) systems. Full article

Photoelectrochemical (PEC) water splitting offers a sustainable route for solar-to-hydrogen conversion, yet its large-scale deployment is often hindered by energy-intensive and costly fabrication processes for semiconductor photoelectrodes. Electrodeposition provides an attractive alternative owing to its solution-based, low-temperature, and scalable nature; however, the relationship between electrochemical deposition parameters and photoelectrode functionality remains insufficiently understood. Herein, we systematically investigate system-level control in electrodeposition for photoelectrode synthesis using BiVO₄ photoanodes and CuO/Cu₂O photocathodes as model systems. By modulating deposition potential, current density, and electrical control modes, we elucidate how interfacial ion dynamics and growth kinetics govern film morphology, phase evolution, and PEC performance. DC electrodeposition establishes a baseline structure–performance relationship governed by precursor concentration and current density, while pulsed operation enables decoupling of nucleation and growth, leading to refined nanostructures and enhanced photocurrent responses. Further incorporation of reverse-pulsed potentials provides dynamic interfacial reset, enabling precise control over porosity and grain connectivity. The optimized BiVO₄ photoanodes fabricated under tailored reverse-pulsed conditions exhibit improved photocurrent density compared to continuously deposited counterparts. The insights presented here provide practical guidelines for rationally engineering high-performance, scalable, and environmentally benign photoelectrodes for PEC water splitting. Full article

Triangular mesh surface representation is widely adopted in geometric design and reverse engineering applications. However, in high-precision Computer Numerical Control (CNC) machining, significant limitations persist in automated Computer-Aided Manufacturing (CAM) tool path generation for such representations. Conventional CAM workflows heavily rely on manual engineering interventions, such as creating drive surfaces or tuning extensive parameters—a dependency that becomes particularly acute for generic free-form models. To address this critical challenge, this paper proposes a novel end-to-end single-step end-milling tool path generation methodology for triangular mesh surfaces in high-precision five-axis CNC machining. The framework includes clustering analysis for optimal workpiece orientation, normal vector distribution analysis to identify shallow and steep regions, Graphics Processing Unit (GPU)-accelerated collision detection for feasible tool orientation domains, and iso-planar tool path generation with Traveling Salesman Problem (TSP) optimization for efficient tool lifting and movement. Experimental validation confirms the framework ensures machining quality and algorithmic robustness. Full article

Vertical shafts are key channels for underground energy storage, mineral exploitation, and related engineering fields. Yet in deeply buried complex strata and high ground stress environments, traditional passive supports are prone to lining failure, while linear yield criteria cannot accurately characterize rock masses’ nonlinear mechanical behavior, limiting their use in shaft analysis. The core mechanical process of shaft construction aligns with the cavity contraction–expansion mechanism: excavation induces cavity unloading and contraction, causing shaft deformation and plastic zone expansion in surrounding rock; support enables cavity reverse expansion via preset shaft wall counter loads to actively control surrounding rock deformation. Based on this, this study integrates the Hoek–Brown nonlinear yield criterion, large-strain theory, and non-associated flow rules; couples cavity contraction–expansion semi-analytical solutions with the composite shaft wall mechanical model; and establishes a composite shaft wall–surrounding rock interaction analysis method. This research clarifies excavation-induced surrounding rock mechanical responses, reveals shaft wall counter loads’ regulatory effect on surrounding rock, and develops a systematic excavation support calculation workflow. Parameter analysis shows that increasing lining thickness is the most direct way to reduce inner wall tensile stress and improve safety; composite linings optimize stress distribution and enhance structural collaborative performance; and safety assessment confirms the lining inner wall as a structural weak zone. The proposed method and findings fill the gap in applying cavity contraction–expansion theory to shaft construction, providing reliable theoretical and practical guidance for deep shaft design, construction, and safety evaluation. Full article

Liquid crystal elastomers (LCEs), as a class of smart materials, have attracted significant attention across soft robotics, biomedical engineering, and intelligent devices because of their unique capabilities to undergo large, reversible, and anisotropic deformations under external stimuli. Over the years, fabrication methods have advanced from conventional molding and thin-film processing to additive manufacturing, with 4D printing emerging as a transformative approach by enabling time-dependent, programmable shape transformations. Among the available methods, direct ink writing (DIW) and vat photopolymerization are most widely adopted, with ink chemistry, rheology, curing, and printing parameters directly governing mesogen alignment and actuation performance. Recent advances in LCE actuators have demonstrated diverse functionalities in soft robotics, including bending, crawling, gripping, and sequential actuation, while biomedical applications span adaptive tissue scaffolds, wearable sensors, and patient-specific implants. This review discusses the conceptual distinction between 3D and 4D printing, compares different additive manufacturing techniques for LCE, and highlights emerging applications in the field of soft robotics and biomedical technologies. Despite rapid progress in LCE, challenges remain in biocompatibility, long-term durability and manufacturing scalability. Overall, innovations in 4D printing of LCEs underscores both the promise and the challenges of these materials, pointing toward their transformative role in enabling next-generation soft robotic and biomedical technologies. Full article

The mechanical performance of laser-welded Ti6Al4V alloy joints is governed by multiple process parameters with complex interplay, leading to nonlinear correlations, that complicate the quest for optimal parameters. In this paper, a reverse engineering model for process parameters was developed using backpropagation (BP) neural networks, targeting mechanical properties as the optimization objective for inverse parameter design. The BP neural network was enhanced via differential evolution tuning, achieving significant improvements in both mechanical property prediction and process parameter inversion. The prediction model demonstrated a relative error of approximately 3%, whereas the inverse model exhibited an error of about 6% under varying process conditions. A novel hybrid BP-WC model was then proposed by fusing weight coefficients from both the prediction and inverse models. This model reduced the inverse error of process parameters to 3%, providing a robust framework for efficient parameter optimization in laser welding for Ti6Al4V alloy. Full article

The design of custom abutments (CA) can affect the mechanical reliability of implant-supported restorations. The purpose of the study was to evaluate the influence of design parameters on the fatigue limit of CA and to compare optimized custom designs with the reference abutment (RA). A morse-tapered dental implant, an anatomical abutment, and a connector screw were digitalized using microcomputed tomography. A cone beam computed tomography scan was obtained from one of the authors to virtually place the implant-abutment assembly in the upper central incisor. Ten design parameters were selected according to the structural geometry of the RA and the implant planning. A reverse-engineered RA model was created in SOLIDWORKS and was modified considering a Taguchi orthogonal array to generate 36 CAs with ±20% dimensional variations. Finite element analysis was conducted in ABAQUS, and fatigue limits were estimated using Fe-safe. ANOVA (α = 0.1) identified the most influential parameters. Von Mises stress values ranged from 229 MPa to 302 MPa, and 94.4% of the CAs had a higher fatigue limit than the RA. Three parameters significantly affected the fatigue performance of the implant system. The design process of custom abutments includes critical design parameters that can be optimized for longer lifetimes of implant-abutment restorations. Full article

The growing consumption of oil and gas resources and the increasing difficulty of extraction have created major challenges for traditional manufacturing and maintenance, particularly in the timely supply of critical components, customized production, and complex structure fabrication. Additive manufacturing (AM) technology, with its high design freedom, precision, and rapid prototyping, provides new approaches to address these issues. However, systematic reviews of related efforts are scarce. This paper reviews the applications and progress of metal and non-metal AM technologies in oil and gas extraction and gathering engineering, focusing on the just-in-time (JIT) manufacturing of failed components, the manufacturing and repair of specialized equipment and tools for oil and gas extraction and gathering, and artificial core and reservoir geological modeling fabrication. AM applications in this field remain exploratory and face challenges with regard to their standards, supply chains, materials, and processes. Future research should emphasize developing materials and processes for extreme conditions, optimizing process parameters, establishing standards and traceability systems, and integrating AM with digital design and reverse engineering to support efficient, safe, and sustainable industry development. This work aims to provide a reference for advancing AM research and engineering applications in the oil and gas sector. Full article