Search Results (91)

The floor acoustic package is a crucial component of a vehicle’s overall acoustic insulation system, and its performance directly influences the interior sound field distribution and acoustic comfort. Conventional investigations of acoustic package performance primarily rely on experimental testing and computer-aided engineering (CAE) simulations. However, these methods often suffer from limited accuracy control, high computational cost, and low efficiency. In contrast, data-driven modeling approaches have recently demonstrated strong potential in addressing these challenges. In this paper, a Squeeze-and-Excitation Residual Network (SE-ResNet) is proposed to predict and analyze the sound insulation performance of vehicle floor systems based on the original structural and material parameters of acoustic package components. By replacing the conventional CAE process with a data-driven framework, the proposed method enhances prediction accuracy and computational efficiency. With the lowest recorded RMSE of 0.4048 dB across the 200–8000 Hz spectrum, the SE-ResNet model ranks first in overall performance. It substantially outperforms the SE-CNN (0.9207 dB) and also shows a clear advantage over both the SE-LSTM (0.4591 dB) and the ResNet (0.4593 dB). Validation using the acoustic package data of a new vehicle model further confirms the robustness of the proposed approach, yielding an overall RMSE = 0.4089 dB and CORR = 0.9996 on the test dataset. These results collectively demonstrate that the SE-ResNet-based method presents a promising and robust solution for forecasting the sound insulation performance of vehicle floor systems. Moreover, the proposed framework offers methodological and technical support for the data-driven prediction and analysis of other vehicle noise and vibration problems. Full article

The structural performance of automotive doors is highly dependent on their metallic support; however, conventional development processes often involve multiple CAD-CAE iterations, which increase lead time and engineering effort. This study presents a methodology for optimizing metallic support geometry by integrating Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), and the Taguchi Design of Experiments (DOE). A Taguchi L16 orthogonal array was used to evaluate eight key geometric factors, including material thickness, fixation point configuration, and geometric reinforcements. Finite element simulations with a meshless solver significantly reduced pre-processing time without compromising accuracy. By analyzing the signal-to-noise (S/N) ratio, the optimal factor combination was identified, which maximized stiffness while minimizing displacement and ensuring robustness against material variability. The optimal design achieved a stiffness of 248 N/mm, a substantial increase over the baseline’s 39 N/mm. This design demonstrates the potential of this methodology to dramatically improve structural performance from the early stages of development and accelerate product development by reducing design iterations. Full article

In order to determine thermally safe driving parameters of ignition coils for hydrogen internal combustion engines (ICE), a reliable estimation of internal power losses is essential. These losses include resistive winding losses, magnetic core losses due to hysteresis and eddy currents, dielectric losses in the insulation, and electronic switching losses. Direct experimental assessment is difficult because the components are inaccessible, while conventional computer-aided engineering (CAE) approaches face challenges such as the need for accurate input data, the need for detailed 3D models, long computation times, and uncertainties in loss prediction for complex structures. To address these limitations, we propose an artificial intelligence (AI)-based framework for estimating internal losses from external temperature measurements. The method relies on an artificial neural network (ANN), trained to capture the relationship between external coil temperatures and internal power losses. The trained model is then employed within an optimization process to identify losses corresponding to experimental temperature values. Validation is performed by introducing the identified power losses into a CAE thermal model to compare predicted and experimental temperatures. The results show excellent agreement, with errors below 3% across the −30 °C to 125 °C range. This demonstrates that the proposed hybrid ANN–CAE approach achieves high accuracy while reducing experimental effort and computational demand. Furthermore, the methodology allows for a straightforward determination of the coil safe operating area (SOA). Starting from estimates derived from fitted linear trends, the SOA limits can be efficiently refined through iterative verification with the CAE model. Overall, the ANN–CAE framework provides a robust and practical tool to accelerate thermal analysis and support coil development for hydrogen ICE applications. Full article

Injection molding is a fundamental process for transforming plastics into various industrial components. Among the critical aspects studied in this process, volumetric contraction and warpage of plastic parts are of particular importance. Achieving precise control over warpage is crucial for ensuring the production of high-quality components. This research explores optimizing injection process parameters to minimize volumetric contraction and warpage in rectangular polypropylene (PP) parts. The study employs experimental analysis, MoldFlow simulation, and Artificial Neural Network (ANN) modeling. MoldFlow simulation software provides valuable data on warpage, serving as input for the ANN model. Based on the Backpropagation Neural Network algorithm, the optimized ANN model accurately predicts warpage by considering factors such as part thickness, flow path distance, and flow path tangent. The study highlights the importance of accurately setting injection parameters to achieve optimal warpage results. The BPNN-based approach offers a faster and more efficient alternative to computer-aided engineering (CAE) processes for studying warpage. Full article

This study investigates a heavy-duty CNC machine tool crossbeam casting manufactured by a leading heavy machine tool producer. A numerical simulation model for the demolding process was developed using proprietary Computer-Aided Engineering (CAE) software. The experimental validation of the residual stress was performed using the blind-hole method on the guide rail mounting surface. The simulation results were compared with experimental data, revealing that the post-demolding simulations exhibited smaller fluctuations than the pre-demolding predictions. The maximum principal stress prediction resulted in an absolute error of 11.8%, effectively reflecting the residual stress distribution for casting design and production optimization. Full article

The use of computer-aided engineering (CAE) tools has become essential in modern design processes, significantly streamlining mechanical design tasks. The integration of optimization algorithms further enhances these processes by facilitating studies on mechanical behavior and accelerating iterative operations. A key focus lies in understanding and mitigating the detrimental effects of cavitation on injector surfaces, as it can reduce the injector lifespan and induce material degradation. By combining advanced numerical finite element tools with algorithmic optimization, these adverse effects can be effectively mitigated. The incorporation of computational tools enables efficient numerical analyses and rapid, automated modifications of injector designs, significantly enhancing the ability to explore and refine geometries. The primary goal remains the minimization of cavitation phenomena and the improvement in injector performance, while the collaborative use of specialized software environments ensures a more robust and streamlined design process. Specifically, using the simulated annealing algorithm (SA) helps identify the optimal configuration that minimizes cavitation-induced effects. The proposed approach provides a robust set of tools for engineers and researchers to enhance injector performance and effectively address cavitation-related challenges. The results derived from this integrated framework illustrate the effectiveness of the optimization methodology in facilitating the development of more efficient and reliable injector systems. Full article

Electrolytic metallic manganese (EMM) is used as an alloying metal to provide resistance to abrasion and corrosion. Highly pure EMM is obtained through electrorecovery or electrowinning. Efforts are ongoing to improve the efficiency and profitability of this process, as 85 to 90% of manganese is produced by the mining industry. This study applied computer-aided engineering (CAE) to provide information on the behavior of the potential distribution at the electrodes in cells separated by membranes, which allows for the optimization of the EMM production process. The experimental results obtained galvanostatically for EMM allowed for validation of the simulation parameters. It was determined that the cell with 11 compartments is more suitable compared to cells with fewer compartments, since it has lower oxidation-normalized current density and oxidation potential, which affect the distribution of cathodic potential in the process of obtaining EMM. The simulation highlighted a better distribution of the cathodic and anodic potentials due to the increase in the number of electrodes. This saves time and resources in the design of electrochemical cells with a greater number of compartments. Full article

The electrification of automobiles makes low-frequency road noise the main factor affecting the performance of automobile NVH (Noise, Vibration and Harshness). High-precision and high-efficiency road noise prediction results are the basis for NVH performance improvement and optimization. However, using the traditional TPA (transfer path analysis) method and CAE (Computer-Aided Engineering) method to analyze the road noise problem has the problems of complex transfer path, difficult acquisition of modeling parameters, long duration and high cost. Therefore, based on the road noise hierarchy constructed according to the road noise transmission path, the LSTM (Long Short-Term Memory) network is introduced to establish a data-driven prediction model, which effectively avoids the defects of the TPA method and CAE in analyzing road noise problems. Based on the LSTM prediction model, the AFW (adaptive feature weight) method is introduced to improve the model’s attention to the key features in the input data and finally improve the accuracy and robustness of the road noise prediction model. The results show that the accuracy (RMSE = 1.74 (dB)) and generalization ability (MAE = 2.60 (dB), $R^2$ = 0.924) of the AFW-LSTM model are better than other models. Full article

This work aimed to develop both a real implementation and a digital twin for a small CNC machining center. The X-, Y-, and Z-axes feed systems were realized as closed-loop motion loops with DC servo motors and encoders. Motion control was provided by Arduino boards and Pololu motor drivers. A simulation study of the step response parameters was carried out, and then the positioning regime was studied, followed by the two-axis simultaneous motion regime (circular interpolation). This study, based on a hybrid simulation diagram realized in Simulink–Simscape, allowed a preliminary tuning of the PID (proportional integral derivative) controllers. Next, the CAE (computer-aided engineering) simulation diagram was complemented with the CAM (computer-aided manufacturing) simulation interface, the two together forming an integrated digital twin system. To validate the contouring performance of the proposed CNC system, a circular groove with an outer diameter of 31 mm and an inner diameter of 29 mm was machined using a 1 mm cylindrical end mill. The trajectory followed the simulated 30 mm circular path. Two sets of controller parameters were applied. Dimensional accuracy was verified using a GOM Atos Core 200 optical scanner and evaluated in GOM Inspect Suite 2020. The results demonstrated good agreement between simulation and physical execution, validating the PID tuning and system accuracy. Full article

In an integrated gasification combined cycle (IGCC), a gasification process produces a gas stream from a solid fuel, such as coal or biomass. This gas (syngas or synthesis gas) resulting from the gasification process contains carbon monoxide, molecular hydrogen, and carbon dioxide (other gaseous components may also be present depending on the gasified solid fuel and the gasifying agent). Separating hydrogen from this syngas stream has advantages. One of the methods to separate hydrogen from syngas is selective permeation through a palladium-based metal membrane. This separation process is complicated as it depends nonlinearly on various variables. Thus, it is desirable to develop a simplified reduced-order model (ROM) that can rapidly estimate the separation performance under various operational conditions, as a preliminary stage of computer-aided engineering (CAE) in chemical processes and sustainable industrial operations. To fill this gap, we present here a proposed reduced-order model (ROM) procedure for a one-dimensional steady plug-flow reactor (PFR) and use it to investigate the performance of a membrane reactor (MR), for hydrogen separation from syngas that may be produced in an integrated gasification combined cycle (IGCC). In the proposed model, syngas (a feed stream) enters the membrane reactor from one side into a retentate zone, while nitrogen (a sweep stream) enters the membrane reactor from the opposite side into a neighbor permeate zone. The two zones are separated by permeable palladium membrane surfaces that are selectively permeable to hydrogen. After analyzing the hydrogen permeation profile in a base case (300 °C uniform temperature, 40 atm absolute retentate pressure, and 20 atm absolute permeate pressure), the temperature of the module, the retentate-side pressure, and the permeate-side pressure are varied individually and their influence on the permeation performance is investigated. In all the simulation cases, fixed targets of 95% hydrogen recovery and 40% mole-fraction of hydrogen at the permeate exit are demanded. The module length is allowed to change in order to satisfy these targets. Other dependent permeation-performance variables that are investigated include the logarithmic mean pressure-square-root difference, the hydrogen apparent permeance, and the efficiency factor of the hydrogen permeation. The contributions of our study are linked to the fields of membrane applications, hydrogen production, gasification, analytical modeling, and numerical analysis. In addition to the proposed reduced-order model for hydrogen separation, we present various linear and nonlinear regression models derived from the obtained results. This work gives general insights into hydrogen permeation via palladium membranes in a hydrogen membrane reactor (MR). For example, the temperature is the most effective factor to improve the permeation performance. Increasing the absolute retentate pressure from the base value of 40 atm to 120 atm results in a proportional gain in the permeated hydrogen mass flux, with about 0.05 kg/m².h gained per 1 atm increase in the retentate pressure, while decreasing the absolute permeate pressure from the base value of 20 bar to 0.2 bar causes the hydrogen mass flux to increase exponentially from 1.15 kg/m².h. to 5.11 kg/m².h. This study is linked with the United Nations Sustainable Development Goal (SDG) numbers 7, 9, 11, and 13. Full article

A gauge plate is a typical thick-walled injection-molded component featuring a complex construction used in high-speed railways, and it is prone to sink voids during the injection process. It is difficult to obtain a void-free injection molded part due to uneven cooling-induced localized thermal gradients, crystallization shrinkage of semicrystalline thermoplastics, fiber orientation-induced anisotropic shrinkage, injection parameter-dependent fountain flow, and inconsistent core compensation. This work employed design of experiment (DOE) and computer-aided engineering (CAE) simulations to analyze the influence of injection parameters on the volumetric shrinkage of the gauge plate and to identify the optimal injection process. A Taguchi orthogonal array L9 was applied, in which four injection molding process parameters were varied at three different levels. The fundamental causes of sink void defects in the gauge plate were then examined via MoldFlow analysis on the basis of the optimized injection parameters. The MoldFlow study indicates a high probability of the presence of sink void defects in the injection-molded gauge plate. To minimize sink void defects, a structural optimization design of the gauge plate was implemented to achieve a more uniform wall thickness, and the advantages of this optimization were demonstrated via comparative analysis. The small batch production of the injection-molded gauge plates demonstrates that the optimized gauge plate shows no sink voids, ensuring consistent quality that adheres to the engineering process and technical specifications. Full article

This paper presents a thorough guide to simulating jet grouting using the Moving Particle Semi-Implicit (MPS) method for numerical analysis and Computer-Aided Engineering (CAE) for model development. It addresses the shortcomings of previous jet grouting simulation studies, which often lacked clear and comprehensive guidelines, by providing a detailed step-by-step approach. The key aspects of the simulation that define and shape the output of real-world jet grouting technology, such as jet grouting spray settings and material parameter configurations, are validated against benchmark experimental data. The previously challenging task of accurately determining material parameters for soil when modeled as a Bingham fluid bi-viscosity model, is simplified into a universal guideline that can be easily applied to any soil type with known unconfined compressive strength. Finally, the reliability of the jet grouting simulation is confirmed by comparing the simulation results with benchmark experimental data under similar conditions, demonstrating the robustness and accuracy of the proposed method. Full article

Metallization, a process for applying anti-corrosion coatings, has advantages over hot-dip galvanizing, such as reduced thermal stress and the ability to work “in situ”. This process consists of the projection of a protective metal as coating from a wire as application material, and this wire is obtained by multi-stage wiredrawing. For the metallization process, a zinc–aluminum alloy wire obtained by this process is used. This industrial process requires multiple stages/dies of diameter reduction, and determining the optimal sequence is complex. Thus, this work focuses on developing models with the aim of designing and optimizing the wiredrawing process of zinc–aluminum (ZnAl) alloys, specifically ZnAl15%, used for anti-corrosion applications. Both analytical models and numerical models based on the finite element method (FEM) and implemented by computer-aided engineering (CAE) software Deform 2D/3D v.12, enabled the prediction of the drawing stress and drawing force in each drawing stage, producing values consistent with experimental measurements. Key findings include the modeling of the material behavior when ZnAl15% wires were subjected to the tensile test at different speeds, with strain rate sensitivity coefficient m = 0.0128, demonstrating that this type of alloy is especially sensitive to the strain rate. In addition, the optimal friction coefficient (µ) for the drawing process of this material was experimentally identified as µ = 0.28, the ideal drawing die angle was determined to be 2α = 10°, and the alloy’s deformability limit has been established by a reduction ratio r ≤ 22.5%, which indicates good plastic deformation capacity. The experimental results confirmed that the development of the proposed models can be feasible to facilitate the design and optimization of industrial processes, improving the efficiency and quality of ZnAl15% alloy wire production. Full article

Additive manufacturing (AM) methods have been gaining momentum because they provide vast design and fabrication possibilities, increasing the accessibility of state-of-the-art hardware through recent developments in user-friendly computer-aided drawing/engineering/manufacturing (CAD/CAE/CAM) tools. However, in comparison to the conventional manufacturing methods, AM processes have some disadvantages, including the machining precision and fabrication process times. The first issue has been mostly resolved through the recent advances in manufacturing hardware, sensors, and controller systems. However, the latter has been widely investigated by researchers with different toolpath planning perspectives. As a contribution to these investigations, the present study proposes a toolpath planning method for AM, which aims to provide highly continuous yet distance-optimized solutions. The approach is based on the utilization of the signed distance field (SDF), clustering, and minimization of toolpath distances among cluster centroids. The method was tested on various geometries with simple closed curves to complex geometries with holes, which provides effective toolpaths, e.g., with relative distance reduction percentages up to 16.5% in comparison to conventional rectilinear infill patterns. Full article

In this paper, we implement the finite detail technique primarily based on T-Splines for approximating solutions to the linear elasticity equations in the connected and bounded Lipschitz domain. Both theoretical and numerical analyses of the Dirichlet and Neumann boundary problems are presented. The Reissner–Mindlin (RM) hypothesis is considered for the investigation of the mechanical performance of a 3D cylindrical shell pipe without and with preformed hole problems under concentrated and compression loading in the linear elastic behavior for trimmed and untrimmed surfaces in structural engineering problems. Bézier extraction from T-Splines is integrated for an isogeometric analysis (IGA) approach. The numerical results obtained, particularly for the displacement and von Mises stress, are compared with and validated against the literature results, particularly with those for Non-Uniform Rational B-Spline (NURBS) IGA and the finite element method (FEM) Abaqus methods. The obtained results show that the computation time of the IGA based on the T-Spline method is shorter than that of the IGA NURBS and FEM Abaqus/CAE (computer-aided engineering) methods. Furthermore, the highlighted results confirm that the IGA approach based on the T-Spline method shows more success than numerical reference methods. We observed that the NURBS IGA method is very limited for studying trimmed surfaces. The T-Spline method shows its power and capability in computing trimmed and untrimmed surfaces. Full article