Search Results (50)

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

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

In the operation of a high-pressure selective catalytic reduction (HP-SCR) system, variations in the internal exhaust gas flow speed result in non-uniform pressure and temperature distribution within the reactor. These fluctuations, which are neither constant nor linear, can affect the safe and reliable operation of the high-pressure selective catalytic reduction (HP-SCR) system, so the strength simulation analysis is necessary. Based on the high-pressure selective catalytic reduction system of a thermo-fluid-solid coupling marine diesel engine as the research object, this study constructs a calculation model using Space Claim and utilizes computational fluid dynamics (CFD) and computer-aided engineering (CAE) numerical simulation methods to analyze the strength of the high-pressure selective catalytic reduction (HP-SCR) reactor. The results show that the overall pressure drop of the selective catalytic reduction system is 5500 Pa, and the overall temperature rise of the reactor is 24 °C, which mainly occurs in the first layer catalyst, accounting for 62.5%. The pressure and temperature load of the reactor change along the axial direction, and the axial deformation gradient of the cylinder is more. The maximum deformation of the reactor under thermal load is 15 times that under mechanical load, and 97% of the deformation is axial. Full article

Wind turbines (WT) are a clean renewable energy source that have gained popularity in recent years. Gearboxes are complex, expensive, and critical components of WT, which are subject to high maintenance costs and several stresses, including high loads and harsh environments, that can lead to failure with significant downtime and financial losses. This paper focuses on the development of a digital twin-based approach for the modelling and simulation of WT gearboxes with the aim to improve their design, diagnosis, operation, and maintenance by providing insights into their behavior under different operating conditions. Powerful commercial computer-aided design tools (CAD) and computer-aided engineering (CAE) software are embedded into a computationally efficient multi-objective optimization framework (modeFrontier) with the purpose of maximizing the power density, compactness, performance, and reliability of the WT gearbox. High-fidelity models are used to minimize the WT weight, volume, and maximum stresses and strains achieved without compromising its efficiency. The 3D CAD model of the WT gearbox is carried out using SolidWorks (version 2023 SP5.0), the Finite Element Analysis (FEA) is used to obtain the stresses and strains, fields are modelled using Ansys Workbench (version 2024R1), while the multibody kinematic and dynamic system is analyzed using Adams Machinery (version 2023.3, Hexagon). The method has been successfully applied to different case studies to find the optimal design and analyze the performance of the WT gearboxes. The simulation results can be used to determine safety factors, predict fatigue life, identify potential failure modes, and extend service life and reliability, thereby ensuring proper operation over its lifetime and reducing maintenance costs. Full article

Computer-aided engineering (CAE) is an essential tool in a digital twin not only to verify and validate a virtual twin before it is transformed into a physical twin, but also to monitor the use of the physical twin for enhanced sustainability. This paper aims to develop a CAE model for a digital twin to predict the fatigue life of materials. Fatigue damage is represented by the size of a macro-crack that grows with a cluster of micro-cracks subjected to three different loads. The growth angle is related to the maximum circumferential tensile stress, and the growth rate is determined by the stress intensity factor (SIF) at the crack tip. The prediction model takes into consideration the main factors, including micro-cracks, crack closures, and initial configurations. Simulations are developed for the growth of macro-cracks with radially distributed micro-cracks and randomly distributed micro-cracks, and we find that (1) the macro-crack in the second case grows faster than that in the first case; (2) a pure shear load affects the macro-crack propagation more than a combined shear and tensile load or a tensional load; (3) the external stresses required to propagate are reduced when the inclination angle of the micro-crack is small and within (−25° < β < 25°); (4) micro-cracks affect the propagating path of the macro-crack and generally guide the direction of propagation. The developed model has been verified and validated experimentally for its effectiveness in predicting the fracture or fatigue damage of a structure. Full article

Fabricating conformal cooling channels has become easier and more cost-effective because of recent advances in additive manufacturing. Conformal cooling channels (CCCs) give better cooling performance than regular (straight drilled) channels during the injection molding process. The main reason for this is that CCCs may follow the paths of the molded shape, but regular channels cannot. CCCs can be used to decrease thermal stresses and warpage while also decreasing cycle time and producing a more uniform temperature distribution. Computer-aided engineering (CAE) simulations are crucial for establishing an effective and cost-effective design. This article focuses on the design optimization of an injection mold, with the goal of optimizing the location of cooling channels to reduce ejection time and increase temperature distribution uniformity. It may be inferred that the created technique is effective and appropriate for the objectives of this work. Full article

For a long time, the traditional injection molding industry has faced challenges in improving production efficiency and product quality. With advancements in Computer-Aided Engineering (CAE) technology, many factors that could lead to product defects have been eliminated, reducing the costs associated with trial runs during the manufacturing process. However, despite the progress made in CAE simulation results, there still exists a slight deviation from actual conditions. Therefore, relying solely on CAE simulations cannot entirely prevent product defects, and businesses still need to implement real-time quality checks during the production process. In this study, we developed a Back Propagation Neural Network (BPNN) model to predict the occurrence of short-shots defects in the injection molding process using various process states as inputs. We developed a Back Propagation Neural Network (BPNN) model that takes injection molding process states as input to predict the occurrence of short-shot defects during the injection molding process. Additionally, we investigated the effectiveness of two different transfer learning methods. The first method involved training the neural network model using CAE simulation data for products with length–thickness ratios (LT) of 60 and then applying transfer learning with real process data. The second method trained the neural network model using real process data for products with LT60 and then applied transfer learning with real process data from products with LT100. From the results, we have inferred that transfer learning, as compared to conventional neural network training methods, can prevent overfitting with the same amount of training data. The short-shot prediction models trained using transfer learning achieved accuracies of 90.2% and 94.4% on the validation datasets of products with LT60 and LT100, respectively. Through integration with the injection molding machine, this enables production personnel to determine whether a product will experience a short-shot before the mold is opened, thereby increasing troubleshooting time. Full article

(1) Background: The purpose of this study was to investigate deformations that occur during additive manufacturing by the HP (Hewlett-Packard) Multi Jet Fusion (MJF) process. These deformations affect the final properties of 3D-printed parts, and proper compensating technology has to be developed in order to minimize these deformations. (2) Methods: Parts were printed with powder composed of nylon plastic infused with glass beads (PA12GB). The HP MJF technology was used during investigations. All parts (specimens) were measured at different points over an extended period to follow the deformations at each point. Different finite element simulations were performed to compare them with real results and assess the viability of using simulations to save time. Various modules of the Digimat software, such as additive manufacturing (AM), material focused (MF), finite element (FE), and computer-aided engineering (CAE), were used to run the simulations. (3) Results: It was found that the printing position of the part in the printer had an impact on deformations. When the part was simulated in a tilted position but alone (deformation: 7.19 mm), the value of the deformation was 1.49 mm greater than when the other parts (two comparable parts) were simulated at the same time (deformation: 5.7 mm). The difference between the simulation with the three parts together (deformation: 5.7 mm) and reality (deformation: 3.44 mm) was 2.26 mm. Finally, the difference between the simulated single part (deformation: 7.19 mm) and the real part (deformation: 3.44) was 3.75 mm. (4) Conclusions: The results of this study will contribute to a better understanding of deformation mechanisms and will suggest solutions for improving the quality of printed parts. Three-dimensional printing is a rapidly growing technology that offers numerous possibilities across various fields. However, one commonly encountered issue is the deformation of printed parts. Methods for minimizing deformations were studied during the 3D printing process using HP MJF technology. Various factors contributing to deformation were investigated, and different techniques for reducing them were explored. Full article

Two- and three-wheeled vehicles account for a significant portion of the automobile market in several countries worldwide. In order to advance the capabilities of these vehicles, the integration of direct-injection (DI) technology is essential, given its potential benefits such as high thermal efficiency and low engine-out emissions. Direct injection in small-bore engines, however, further complicates the challenges involved (of DI technology) like fuel impingement and mixture inhomogeneity inside the engine cylinder, driving the need for an in-depth exploration of in-cylinder processes. Consequently, the necessity arises to develop a small-bore direct-injection spark-ignition (DISI) optical engine that incorporates a transparent cylinder and piston top. In this scenario, these transparent components are required to endure a combination of intricate loads and boundary conditions, hence the potential to result in failures. This work aims to assess numerically the effects of these loads and boundary conditions on the transparent components and optimize their thicknesses. For this purpose, a computer-aided design model of a small-bore DISI optical engine (displacement volume of 200 cm³) is developed. The mechanical and thermal loads are extracted from the experimental data and validated computational fluid dynamics model of the same engine configuration. A coupled temperature-displacement finite element analysis methodology is developed in ABAQUS/CAE, and simulations are performed under both steady and transient conditions. Temperature and combined stress distributions within the transparent cylinder and piston top are obtained and analyzed to find their optimum thicknesses. Knowing thermal gradients, combined stresses and displacements under actual conditions helped design the small optical engine with an improved factor of safety. Full article