Browse Topic: CAD, CAM, and CAE
The rapid electrification of the automotive industry introduces new challenges in noise, vibration, and harshness (NVH). In particular, in a virtual prototyping phase of the e-vehicles development, the rubber mounts are often one of the key elements to be considered when analysing the structure borne noise contributions. Having an accurate experimental characterization of the mount dynamic stiffness curves is therefore very relevant. However, conventional mount characterization methods are often pushed to their limits, partly due to the use of stiffer bushings, and partly because the frequency range of interest is extended toward higher frequencies. When using inverse substructuring, the dynamic stiffness curves can be obtained from frequency response function measurements. The required test setup consists of excitations and responses, located on each side of the mount via dedicated fixtures. The measured frequency response functions are reduced into 6 degrees of freedom representation at the active and passive side of the mount using the classical virtual point transformation. This classical approach assumes the fixtures to behave rigidly. This assumption holds in the lower frequency range, but not anymore in the higher frequency range. In this paper, novel approaches to identify the dynamic stiffness are presented. Namely, an enhanced virtual point transformation that considers flexible fixtures modes is proposed. Those modes may be obtained via finite element modeling or from an experimental modal analysis. Alternatively, a hybrid framework leveraging high-frequency testing and simulation to develop a parametric finite element mount model is presented. The latter approach eliminates the need for fixtures. These methodologies are compared and validated on an automotive rubber mount.
In recent years, computer-aided engineering (CAE) has become an essential practice in design and durability analysis of industrial components such as weldments. The current analytical trend for CAE-based fatigue life prediction of weldments includes procedures based on design guidelines, mesh-sensitive methods (e.g., local strain-life approach) and mesh insensitive methods (e.g., Volvo and Verity methods). As an inherent characteristic of weldments, the geometry of the weld is often simplified in failure analysis and important hotspots such as start/stop of the weld beads are not considered in the design process. However, such critical locations cannot be avoided in complex welded structures. Therefore, incorporating main geometrical details of the weld can improve the accuracy of critical regions identification and damage calculation using mesh-sensitive CAE-based methodologies. Herein, a framework for life prediction of welded components including the weld geometry is discussed and evaluated by its application to a coupled torsion beam axle. The weldment was simulated in finite element (FE) environment as a shell model with local mesh refinement and improved weld geometry. The FE model was validated by strain gage measurements of the actual component under single-channel constant amplitude load and critical locations in the component were accurately identified. Local stress-life and critical plane approaches were employed to predict fatigue life to failure resulting in reasonable accuracy within a factor of two. Despite the close results by the uniaxial and multiaxial fatigue damage criteria in this work, advanced life prediction approaches such as the critical plane concept are recommended due to their robustness for more complex and realistic loading conditions during service.
The design of thermal components (such as automotive heat exchangers) requires balancing multiple competing objectives—thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design workflows rely on manual Computer Aided Design (CAD) modeling and iterative simulations, which are both labor-intensive and time-consuming. Recent advances in Large Language Models (LLMs) present untapped potential for automating parametric CAD generation. However, current LLM-based approaches primarily handle simple, isolated geometric primitives rather than complex multi-component assemblies. This work introduces a progressive framework that leverages fine-tuned LLMs (Qwen2.5-3B-SFT) integrated with the CadQuery CAD kernel to automatically generate parametric geometries from natural language descriptions. As a foundational study, this work focuses on Step 1 of the framework: generating and optimizing isolated geometric primitives (cylinders, pipes, etc.) that form the building blocks of complex assemblies. The generated models are automatically exported to standard CAD formats and seamlessly integrated within a multi-objective Bayesian optimization pipeline using Gaussian Process regression. By decoupling natural language-driven CAD code generation from traditional manual scripting, this work demonstrates how LLMs can accelerate design space exploration while eliminating the need for engineers to write geometry-specific Python scripts. Case studies on parametric pipe optimization demonstrate the framework's efficiency gains and establish a foundation for future steps: handling constraints, multi-component assemblies, and full thermal component designs. This work contributes to next-generation Artificial Intelligence (AI) assisted engineering design by demonstrating LLM-powered automation as a practical pathway toward fully automated design-to-optimization workflows.
Industries are following a tedious product development cycle for developing their product. In product development major steps includes design ideas, Drawings, CAD, CAE, Testing and design improvement cycle. This is a monotonous process and takes time which impacts on its time to deliver product and cost on development. Now a days industries are fast growing and targeting to reduce development cycle time and cost. AI&ML is impacting almost all areas in the industry and significantly reducing efforts time and cost. To make use of AI&ML in CAE, Altair Physics AI is an effective tool. To ensure the design of product traditional way is to develop a CAD of the product, develop, perform CAE and analyze performance. If we consider CAE procedure it is time consuming process which includes FEA model build, applying boundary conditions, running simulation and analyzing results which could take minutes to hours. By using ML with Physics AI we can make predictions on new design of the product in seconds and significantly save time and cost. To demonstrate the CAE acceleration process with physic AI we have solved two case studies. The first case study is head impact on hood where ML tool will predict deformation contour of the hood, acceleration and displacement curve of the impactor. The second case study is Tube crush analysis where prediction of tube deformation pattern, force and energy curve for different tube length and impact velocity is carried out. For both Case studies we have used TCS inhouse data to train test and prediction of the ML model. For Head impact case study, it gives lower training loss with more than 90 percent prediction accuracy. Similarly for tube crush study it gives good accuracy and predicts comparable behavior patten with CAE results. Physic AI ML tool accelerates the design and development cycle and can be utilized in different product development. Implementation of ML accelerates the CAE process in design and development of products. It saves a lot of time in multiple design iteration study. Similar method can be implemented for different CAE cases.
Thermal and lubrication management is critical for the performance characteristics of Electric Drive Units (EDUs) in electrified powertrains. Accurate assessment of lubrication flow, particularly in terms of wetting behavior and churning losses, is essential for optimizing EDU performance across various driving conditions. This study presents a comprehensive numerical investigation of lubrication flow behavior within an EDU using an advanced Smoothed Particle Hydrodynamics (SPH) method. The mesh-free SPH approach provides significant advantages in modeling intricate oil dynamics, such as oil splashing, and the behavior of oil in contact with rotating components. The primary focus of this study is to investigate the phenomena of oil splashing, wetting behavior characterized by the Wetting Fraction(WF), and churning losses within the gearbox environment. Key flow characteristics such as oil distribution, particle trajectories, torque resistance due to fluid drag, and oil volume fraction are analyzed under varying operational parameters. The EDU design is then refined through multiple design iterations using the SPH method to enhance splashing characteristics and improve WF for critical components. This work demonstrates the effectiveness of the SPH method as a robust virtual prototyping tool for next-generation EDU lubrication system design.
This paper discusses the design of a 2000-lb manned eVTOL aircraft propelled by a novel cycloidal rotor propulsion system. To systematically evaluate the performance of the proposed configuration, a coupled trim model was developed to quantitatively evaluate the performance of the configuration across a range of forward flight speeds. The trim framework integrates an efficient physics-guided neural-network-based aerodynamic model for cycloidal rotor performance with a vehicle-level dynamic response model. This framework is used to conduct a systematic parametric study to identify key cycloidal rotor and airframe design parameters. The selected configuration is verified using high-fidelity CFD simulations, and a detailed structural design, powertrain design, and CAD model of the aircraft is developed. In addition to CFD validation, the proposed cycloidal rotor underwent structural optimization to confirm the validity of such a concept at this scale. The results demonstrate that the cycloidal rotors provide a viable propulsion alternative for eVTOL aircraft with strong potential to overcome limitations of existing configurations.
In pursuit of a distinct sporty interior sound character, the present study explores an innovative strategy for designing intake systems in passenger vehicles. While most existing literature primarily emphasizes exhaust system tuning for enhancing vehicle sound quality, the current work shifts the focus toward the intake system’s critical role in shaping the perceived acoustic signature within the vehicle cabin. In this research work, target cascading and settings were derived through a combination of benchmark and structured subjective evaluation study and aligning with literature review. Quantitative targets for intake orifice noise was defined to achieve the desired sporty character inside cabin. Intake orifice targets were engineered based on signature and sound quality parameter required at cabin. Systems were designed by using advanced NVH techniques, Specific identified acoustic orders were enhanced in the intake system to reinforce the required signature in acceleration as well as in cruising mode. A novel decomposition method was developed to identify exact contribution of intake system’s noise from overall in cab noise. Based on advanced NVH analysis and sound diagnosis a precise identification of intake system contributions during both acceleration and cruising conditions was carried out. Furthermore, sound design strategy was developed which targets a dual-mode acoustic profile. The developed design strategy was validated at vehicle level, confirming that the intake system design met both subjective and objective targets. This integrated approach provides a repeatable framework for intake sound design, offering OEMs a robust pathway to differentiate sporty vehicle character through intelligent intake acoustics. This work not only demonstrates the critical role of intake design in vehicle sound signature development but also proposes a systematic methodology for future vehicle sound engineering.
In area of modern manufacturing, ensuring product quality and minimizing defects are utmost important for maintaining competitive advantage and customer satisfaction. This paper presents an innovative approach to detect defect by leveraging Artificial Intelligence (AI) models trained using Computer-Aided Design (CAD) data. Traditional defect detection methods often rely on physical inspection, which can be time-consuming and prone to human error. The conventional method of developing an AI model requires a physical part data, By utilizing CAD data, the time to develop an AI model and implementing it to production line station can be saved drastically. This approach involves the use of AI algorithms trained on CAD models to detect and classify defects in real-time. The field trial results demonstrate the effectiveness of this approach in various industrial applications, highlighting its potential to revolutionize defect detection in manufacturing.
The objective of this paper is to evaluate the thermal performance of the brake discs in the design stage of its life cycle by developing a methodology to replicate dynamometer testing using multi-disciplinary Finite Element Analysis (FEA) methods. A simulation workflow was formulated in which Computational Fluid Dynamics (CFD) was used to create temperature and velocity dependent Heat Transfer Coefficients (HTC) which were in turn used in Computer Aided Engineering (CAE) to do a thermo-mechanical analysis. With this workflow various designs of the brake discs were analyzed. A sensitivity study was done to determine critical design features that affected its thermal performance. A final design was fixed that met both the weight and thermal performance targets. This design was evaluated in dynamometer testing, and 93% correlation was achieved. Thus, the developed simulation workflow ensured that a first-time right brake disc can be finalized in the design stage, which will meet the performance in dynamometer testing.
Simulation-driven product development involves numerous computer aided engineering (CAE) model iterations, where each version represents a critical difference. Usually, these multiple model versions are generated by hundreds of simulation engineers working in teams distributed across the globe, making functional collaboration a key to effective product development. To manage vast amounts of CAE data generated by engineers working simultaneously on a project, it is imperative to have a robust version management system to track changes in the CAE data. A robust version management is the backbone of an effective simulation data management (SDM) system. It involves capturing and documenting model changes at every design iteration. Accurate documentation of the model changes is crucial as it helps in understanding the model evolution and collaboration among engineers. However, documenting is usually considered a boring and tedious task by many engineers. This often leads to bad change documentation, which in turn reduces data discoverability and causes knowledge loss. With the onset of artificial intelligence (AI) in engineering simulations, engineers can now learn even more from their simulation data. In this paper, authors have explored an AI-assisted approach for facilitating the change documentation by augmenting the change comments via automatically extracted details, as studied in the SAFECAR-ML research project. The project is funded by the German Federal Ministry of Education and Research (BMBF) under the “KI4KMU” initiative (Research, Development, and Use of AI Methods in SMEs). The main goal of SAFECAR-ML is to develop an AI model that understands the nature of design changes and automatically generates change descriptions. When a detailed and informative change documentation is available, large language model (LLM)-based generative AI can be used for discovering and creating simulation-related content in an SDM system, for example by using retrieval augmented generation (RAG) approaches. A long-term outlook is to build an AI-assisted capability to perform complex tasks in an SDM system, like search and summarization of the data, automatic evaluation of simulation results, and thinking models for researching the available simulation data making recommendations on further model changes.
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