Characterization and FEA Based Optimization of Elastomeric Components for Automotive Applications

A data-driven optimization framework that integrates machine learning surrogate models, finite element analysis (FEA), and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer (TPE) parts for aerospace applications. By using FEA simulations and experiments, a database of input design parameters (e.g., geometry and structural shape modifier) is generated. Afterwards, we train surrogate models (e.g., Gaussian Process Regression, neural networks) to approximate mappings from design space to performance space. Finally, we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop (such as NSGA-II or gradient-based methods). The novelty of this approach is demonstrated by employing highly simplified surrogate models, including an artificial neural network (ANN) with 10 hidden neurons trained on analytically generated synthetic data. The proposed methodology has been validated using an aerospace-related case study: a vibration-damping plate. Compared with the baseline configuration, Pareto-optimal designs identified by the proposed framework achieved a reduction in maximum deflection of 23%–28% and a reduction in von Mises stress of 18%–24%, depending on the selected trade-off solution, as the number of full FEA simulations required for optimization was reduced from 500 to 50. This framework enables faster design of TPE components for aerospace systems. Validation against high-fidelity ANSYS simulations showed a mean error of ~1.18% and a maximum deviation of ~2.6%.

The warpage of individual IC panel/package or whole molded strips keeps changing with temperature during the assembly processes from die bonding, wire bonding, molding, post-mold cure to soldering reflow. How to optimize package design and properties of all assembly materials to obtain acceptable warpage level both at room temperature and reflow temperature becomes more challenging. In this study, finite element analysis (FEA) simulation was performed in warpage prediction of three types of advanced laminated IC packages (PBGA, CSP BGA and PoP BGA). Shadow Morie system was used in monitoring warpage deviation under temperature profile from -55degC to 260degC. Calibration or correction of FEA results can be performed by introducing experimental results from Shadow Morie measurement to fine-tune the modeling so as to obtain more accurate prediction

There are three materials that are commonly used in commercial glazing seals: EPDM, silicone and thermoplastic vulcanizates (TPVs). TPVs are a high performance class of thermoplastic elastomers (TPEs), where TPEs have elastomeric properties with thermoplastic processability. TPVs have emerged as materials well suited for use in glazing seals due to ease of processing, economics and part design flexibility. The part design and development process is critical to ensure that the chosen TPV provides economics, quality and function in demanding environments. In the design and development process, there is great value in utilizing dual durometer systems to capitalize on the benefits of soft and rigid materials. Computer-aided design tools, such as Finite Element Analysis (FEA), are effective in minimizing development time and predicting system performance. Examples of TPV glazing seals will illustrate the benefits of utilizing FEA to take full advantage of the material characteristics, which results in functional performance and quality while reducing development iterations. FEA will be performed on two glazing seal profiles to confirm optimum geometry.

Conventional finite element analysis (FEA) performed in electromagnetic-vibration coupling calculation of motor suffers from several significant drawbacks, such as large memory space, high computational cost and heavy reliance on mesh quality for accurate solution. With the traditional meshless method, special attention needs to be paid to correctly impose the boundary condition like FEA. Besides, the matrix is easily prone to be ill-conditioned due to introducing large amount of higher-order basis functions. We propose a novel multi-physical coupling method combining FEA and optimized meshless method. The proposed methodology is further evaluated on the vibration analysis of a 12-slot 10-pole permanent magnet synchronous motor (PMSM). Firstly, 2D stator electromagnetic force is simulated and derived based on the local Jacobian derivative method through FEA. The electromagnetic force spectrum is calculated using FFT analysis and further imported into commercial meshless structural simulation software SimSolid for stator harmonic response analysis. Correct force boundary condition and data mapping between meshless and FEA simulation interface are key to the accuracy of the proposed combined multi-physical modeling methodology. This is achieved by introducing new high-dimensional ramp function in the transition region between FEA and meshless domains, which are defined with the shape functions composed of the FEA and meshless method. This function satisfies the continuity and consistency of the displacement function and ensures the convergence of coupled FEA-meshless method. Subsequently, construction of basis function is key to the establishment of convention meshless discrete equation for the elastic problem of rotating machinery. This is designed by using moving least square theory in cylindrical coordinate system. A harmonic response with meshless method is analyzed by using the mode superposition method to obtain detailed mode shape data, acceleration and displacement distribution of stator. Finally, the tangential continuity and robustness are not well considered in the traditional simulation with FEA coupled meshless method. To mitigate this problem, we propose an optimized meshless method based on modified local basis functions to recalculate the harmonic response motion. Then, the coupling electromagnetic-vibration simulation results of traditional coupled FEA-meshless method, optimized coupled FEA-meshless method and complete FEA coupled method are compared. It is worth noting that the optimized method significantly improves accuracy, robustness and computational speed at the same time. In short, the proposed electromagnetic-structure coupling calculation method provides a novel alternative for the multi-physical coupling calculation of rotating machinery combining FEA and meshless simulation methods.

Although a compliant gripper is a type of compliant mechanism, existing analytical models rarely account for the real mechanical behavior of the flexible parts within the gripper. In this work, an in-plane motion flexure-based compliant gripper is presented, modeled, and analyzed. An analytical methodology based on the Compliance Matrix Method (CMM) is proposed to compute the relationship between input and output forces and displacements in two different coordinate frames using a single unified expression that represents the real mechanical behavior of the system. The analytical model is validated through Finite Element Analysis (FEA) simulations and experimental tests performed on additively manufactured prototypes. Additionally, the model is used to analyze output displacements resulting from variations in geometric parameters, with results validated through FEA simulations. This demonstrates the method’s utility in optimizing gripper design during early development stages prior to fabrication. The proposed analytical model accurately estimates the compliant gripper’s kinetostatic behavior, achieving less than 5% difference compared to FEA and experimental results. Furthermore, the model is adaptable and can be extended for the kinetostatic analysis of other compliant grippers with similar flexure-based architectures.

Optimal parameter selection is a crucial step in improving the quality of electronic packaging processes. Traditional approaches usually start with a set of physical experiments and then employ Design of Experiment (DOE) based response surface methodology (RSM) to find the parameter settings that will optimize a desired system response. Nowadays deterministic computer simulations such as Finite Element Analysis (FEA) are often used to replace physical experiments when evaluating a system response, e.g., the stress level in an electronic packaging. However, FEA simulations are usually computationally expensive due to their inherent complexity. In order to find the optimal parameters, it is not practical to use FEA simulations to calculate system responses over a large number of parameter combinations. Nor will it be effective to blindly use DOE-based response surface methodology to analyze the deterministic FEA outputs. In this paper, we will utilize a spatial statistical method (i.e., the Kriging model) for analyzing deterministic FEA outputs from an electronic packaging process. We suggest a sequential method when using the Kriging model to search for the optimal parameter values that minimize the stress level in the electronic packaging. Compared with the traditional RSM, our sequential parameter selection method entertains several advantages: it can remarkably reduce the total number of FEA simulations required for optimization, it makes the optimal solution insensitive to the choice of the initial simulation setting, and it can also depict the response surface and the associated uncertainty over the entire parameter space.

The interfacial stress and its distribution affect directly the mechanical properties of polymeric blends and composites. Polymers are usually modified with rubber to improve their toughness. The three-dimensional (3D) finite element analysis (FEA) of the interfacial stress distribution between the particulate and the matrix during tensile of a rubber-modified polypropylene (PP) blend is made by means of an ANSYS software in this article, and the results are compared with those of the two-dimensional (2D) FEA simulation published earlier. The results show that the shear effect subjected in the pole zone of the rubber particle is maximal, the equator zone of the rubber particle is strongly pressed and pulled, and the tensile stress reaches the maximum. Comparatively, the stress distribution of 3D FEA is more gentle than that of 2D FEA.

Acoustic emission (AE) testing and Lamb wave inspection techniques have been widely used in non-destructive testing and structural health monitoring. For thin plates, the AEs arising from structural defect development (e.g., fatigue crack propagation) propagate as Lamb waves, and Lamb wave modes can be used to provide important information about the growth and localisation of defects. However, few sensors can be used to achieve the in situ wavenumber–frequency modal decomposition of AEs. This study explores the ability of a new multi-element piezoelectric sensor array to decompose AEs excited by pencil lead breaks (PLBs) on a thin isotropic plate. In this study, AEs were generated by out-of-plane (transverse) and in-plane (longitudinal) PLBs applied at the edge of the plate, and waveforms were recorded by both the new sensor array and a commercial AE sensor. Finite element analysis (FEA) simulations of PLBs were also conducted and the results were compared with the experimental results. To identify the wave modes present, the longitudinal and transverse PLB test results recorded by the new sensor array at five different plate locations were compared with FEA simulations using the same arrangement. Two-dimensional fast Fourier Transforms were then applied to the AE wavefields. It was found that the AE modal composition was dependent on the orientation of the PLB direction. The results suggest that this new sensor array can be used to identify the AE wave modes excited by PLBs in both in-plane and out-of-plane directions.

Enhancing power density and reliability of power electronics is extremely important in power electronics applications. One of the key challenges in the design process is to design the optimum heat sink. In this paper, an algorithm is proposed to design air-cooled heat sinks using genetic algorithm (GA) and finite element analysis (FEA) simulations. While the GA generates a population of candidate heat sinks in each iteration, FEA simulations are used to evaluate the fitness function of each. The fitness function considered in this paper is the maximum junction temperature of the semiconductor devices. With an approach that prefers “survival of the fittest”, a heat sink providing better performance than the conventional heat sinks is obtained. The simulation and experimental evaluations of the optimized air-cooled heat sink are also included in the paper.

Recent developments in Generative AI enable creators to stylize 3D models based on text prompts. These methods change the 3D model geometry, which can compromise the model’s structural integrity once fabricated. We present MechStyle, a system that enables creators to stylize 3D printable models while preserving their structural integrity. MechStyle accomplishes this by augmenting the Generative AI-based stylization process with feedback from a Finite Element Analysis (FEA) simulation. As the stylization process modifies the geometry to approximate the desired style, feedback from the FEA simulation reduces modifications to regions with increased stress. We evaluate the effectiveness of FEA simulation feedback in the augmented stylization process by comparing three stylization control strategies. We also investigate the time efficiency of our approach by comparing three adaptive scheduling strategies. Finally, we demonstrate MechStyle’s user interface that allows users to generate stylized and structurally viable 3D models and provide five example applications.

Thermoplastic polyurethane (TPU) is one of the most widely used thermoplastic elastomers (TPE) due to its high strength and tear resistance, good elasticity, flexibility and damping properties. Enhancement of TPU properties can be achieved by either varying the hard-soft segment composition ratio or by the reinforcement with micro or nanoscale fillers. In the last decade, cellulose nanocrystals (CNC) have gained an increasing degree of interest from both academics and industries as ‘sustainable nanomaterials’, as they have high-axial mechanical properties and reinforcing capability, abundance, low density, renewability and biodegradability. Contemporary research activities centred on the reinforcement of TPU with CNC have typically employed solvent-based fabrication methods (solution/wet casting). For the successful translation of this new class of nanocomposite materials from laboratory research to widespread applications, processing via scalable approaches has to be demonstrated. The utility of CNC as TPU reinforcers has also been limited by the poor thermal stability of CNC, which are typically isolated via acid hydrolysis, as well as the poor quality of dispersion achieved when more scalable methodologies are used (eg. melt compounding). Thus, this study firstly aims to explore, optimise and develop CNC with enhanced thermal stability, and secondly, to incorporate these more thermostable CNC into high performance TPU nanocomposites via scalable melt-compounding and reactive extrusion methods. The research thesis is presented in three main parts. The first part is focused on the production/isolation of CNC with enhanced thermal stability and at a high production yield. CNC has been isolated via two methods, acid hydrolysis using mild acid and a scalable high energy bead milling process. Both processes have been optimised to isolate CNC with requisite thermal stability and dispersibility for TPU host polymers. The second part is focused on the processability of CNC into the TPU matrix via an intermediate-scale traditional twin screw extrusion melt compounding method. The third and final part is focused on the processing TPU/CNC nanocomposites via a large-scale reactive extrusion method, by pre-dispersing CNC in polyol and then performing in-situ polymerisation in a twin-screw extruder (i.e. reactive extrusion). The main outcomes or observations from this study are: • Isolation of CNC with enhanced thermal stability and dispersibility via mild acid hydrolysis, and with a higher production yield than the previously reported methods • Acid-free isolation of CNC with enhanced thermal stability at high production yields • Organic solvent-free processing of TPU/CNC nanocomposites • The performance of the host TPU matrix, a “workhorse” aromatic polyether grade, is remarkably enhanced by the incorporation strategies reported, without negatively affecting the important elastic properties and compliance of the TPU

This article aims to investigate the sound absorbing properties of a thick microperforated panel (MPP) with tapered sections with finite element analysis (FEA) models. The FEA model was validated by using the measured sound absorption coefficients of a classic MPP sample and a proposed MPP sample. The FEA simulation and the experiments indicate that the tapered section can enhance the sound absorption coefficient. Moreover, the FEA model shows that the structural parameters of the tapered section can be optimized. The resonance frequency of the sound absorption coefficient moves to the high-frequency range, and the maximum sound absorption coefficient increases in three conditions, the tapered section moving toward the backing cavity, and the increase of the thickness and the bottom radius of the tapered section. Although the optimized configurations of the tapered section may vary with the structure parameters of the MPP, the tapered section can improve the sound absorbing properties of the classic MPP and could be promising in the noise and vibration engineering projects.

Warpage is a major reliability concern for IC packages. Package structure, material property and assembly process will all impact warpage behavior, which can be characterized by 3D Finite Element Analysis (FEA). Traditionally, the FEA simulation will only analyze warpage of a free standing package with thermo-mechanical stress induced by Coefficient of Thermal Expansion (CTE) mismatch during cooling process of Epoxy Molding Compound (EMC) from cure temperature to room temperature. In this paper, a dedicated FEA model has been developed to simulate a strip type Fine-Pitch Ball Grid Array (FBGA) package. Different from conventional simulation, effect of both EMC chemical shrinkage due to polymerization conversion during post molding cure and actual measurement operation on warpage have been taken into consideration in the FEA model. Parametric study with different package layout, die area and die thickness has been performed. Result showed package layout of the strip can significantly impact warpage; and die area had more influence on warpage than die thickness. Warpage performance can be improved by optimizing these parameters. Compare to measurement, the simulation result is in good agreement with actual package warpage.

Since the temperature rise is one of the key parameters in the design of magnetic components, thermal models are commonly applied in order to calculate the temperature value. Finite element analysis (FEA) tools are typically used to visualize thermal distributions but not for obtaining a model from the field calculation. This work presents a new thermal model based on the use of a thermal network whose parameters are obtained from 2D/3D FEA analyses. The model is suitable for any geometry and any thermal distribution. Comparisons of the proposed model with measurements and FEA simulations are also presented.

PowerSynth is a multiobjective optimization tool for rapid design and verification of power semiconductor modules. By using reduced order models for the calculation of electrical parasitics of the layout and thermal coupling between devices, optimal trace layout and die placement can be simultaneously achieved orders of magnitude faster than conventional finite element analysis (FEA) techniques. An overview of the tool, its modeling methods, model validation, and module layout optimization are presented. The electrical and thermal models are validated against FEA simulations and physical measurements of built modules generated from the tool. The FEA comparisons are performed with FastHenry and ANSYS Icepak to evaluate electrical parasitics and thermal behavior, respectively. A sample hardware prototype based on a half-bridge circuit topology is chosen for testing. Excellent agreement between the FEA simulations, experimental measurements, and PowerSynth predictions are demonstrated. Additionally, when compared with conventional simulation runtime and workflow, PowerSynth takes considerably less computation and user time to produce several candidate layout solutions from which a designer may easily balance selected tradeoffs.