Browse Topic: CAD, CAM, and CAE
In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors
Physical testing is required to assess multiple vehicles in different conditions, specially to validate those related to regulations. The acoustic evaluations have difficulties and limitations in physical test; cost and time represent important considerations every time. Additionally, the physical validation happens once a prototype has been built, this takes place in a later phase of the development. Sound pressure is measured to validate different requirements in a vehicle, horn sound is one of these and it is related to a regulation of united nations (ECE28). Currently the validation happens in physical test only and the results vary depending on the location of the horn inside the front end of every vehicle. [7] In this article, the work for approaching a virtual validation method through CAE is presented with the intention to get efficiency earlier in product development process.
The design of drive units in electric vehicles (EVs) presents challenges due to the need to pass multiple linear and non-linear load cases. This can result in inefficient design. Therefore, optimization plays a critical role in improving the design efficiency. However, setting up the optimization process itself can be challenging, especially when dealing with complex design variables and different load cases that require the use of various computer-aided engineering (CAE) solvers. The drive unit, being a casting component, presents additional challenges in setting up Multidisciplinary Design Optimization (MDO) process. This paper introduces an efficient process for addressing these challenges by presenting a sample Multidisciplinary Design Optimization (MDO) problem. The problem involves the manipulation of discrete design variables, such as the number of ribs, and incorporates five different load cases that require the utilization of different CAE solvers. The proposed process
Spray washing is commonly used in car manufacturing to clean and prepare surfaces for subsequent processes like coating and painting. It uses high-pressure spray to deliver cleaning solutions or water onto vehicle surfaces to remove dirt, oils, metal shavings, and contaminants. For optimal washing quality, it is important to have proper nozzle arrangements, spray configuration, and vehicle positioning. Numerical simulations can be used to minimize the trial-and-error process and improve the quality. Spray washing involves strong discontinuities, fragmentation, violent free-surface changes, and complex multiphase flow, which are difficult to simulate using conventional grid-based methods. Lagrangian differencing dynamics (LDD) is a novel numerical method which has the features of being Lagrangian, meshless, and second-order accurate. It employs a meshless finite difference approximation scheme over scattered points and solves the incompressible Navier-Stokes equations in an implicit way
This paper reports on the development of a simulation model to predict engine blowby flow rates for a common rail DI diesel engine. The model is a transient, three-dimensional computational fluid dynamics (CFD) model. Managing blowby flow rates is beneficial for managing fuel economy and oil consumption. In doing so, an improved understanding of the blowby phenomenon is also possible. A mesh for the sub-micron level clearances (up to 0.5 microns) within the piston ring pack is created using a novel approach. Commercial CFD software is used to solve the pressure, velocity, and temperature distributions within the fluid domain. Ring motions within the piston grooves are predicted by a rigorous force balance. This model is the first of its kind for predicting engine blowby using a three-dimensional simulation model while solving the complete set of governing transport equations, without neglecting any terms in the equations. The predicted blowby flow rate has been validated with
The main purpose of the semi-active hydraulic damper (SAHD) is for optimizing vehicle control to improve safety, comfort, and dynamics without compromising the ride or handling characteristics. The SAHD is equipped with a fast-reacting electro-hydraulic valve to achieve the real time adjustment of damping force. The electro-hydraulic valve discussed in this paper is based on a valve concept called “Pilot Control Valve (PCV)”. One of the methods for desired force characteristics is achieved by tuning the hydraulic area of the PCV. This paper describes a novel development of PCV for practical semi-active suspension system. The geometrical feature of the PCV in the damper (valve face area) is a main contributor to the resistance offered by the damper. The hydraulic force acting on the PCV significantly impacts the overall performance of SAHD. To quantify the reaction force of the valve before and after optimization under different valve displacements and hydraulic pressures were simulated
The modern luxurious electric vehicle (EV) demands high torque and high-speed requirements with increased range. Fulfilling these requirements, arises the need for increased electric current supply to motors. Increased amperage through the stator causes higher losses resulting in elevated temperature across the motor components and its housing. In most of the cases, stator is mounted on the housing through interference fit to avoid any slippage during operation conditions. High temperature across the stator and housing causes significant thermal expansions of the components which is uneven in nature due to the differences in corresponding coefficient of thermal expansion (CTE) values. Housings are generally made of aluminium and tends to expand more having higher value of CTE than that of steel core of stator which may give rise to a failure mode related to stator slippage. To address this slippage if the amount of interference fit is increased, that’ll result in another failure mode
In the automotive industry, the durability and thermal analysis of components significantly impact vehicle component robustness and customer satisfaction. Traditional computer-aided engineering (CAE) methods, while effective, often involve extensive design iterations and troubleshooting, leading to prolonged development times and increased costs. The integration of artificial intelligence (AI) and machine learning (ML) into the CAE process presents a transformative solution to these challenges. By leveraging AI and ML, the durability simulation time of automobile components is significantly enhanced. Altair’s Physics AI tool utilizes historical CAE data to train ML models, enabling accurate predictions of model performance in terms of durability and stiffness. This reduces the necessity for multiple simulations, thereby decreasing CAE model design and solution completion times by 30%. By predicting potential issues early in the design phase, AI and ML allow engineers to make informed
The Tractor is essential in both agriculture and construction, equipped with a variety of implements for different operational conditions. Its hydraulic system is crucial for controlling these implements during fieldwork and transport. The quadrant assembly is a key part of the tractor’s hydraulic control system, allowing the operator to manage important functions. This includes hydraulic control and draft control, enabling the farmer or operator to use the PC and DC levers to adjust the movement of implements during various tasks. Tractors are commonly used in fields and farms where the soil can be loose and muddy, particularly during wet puddling operations. In these muddy conditions, tractors can accumulate mud in critical components, such as the quadrant assembly. This can lead to functional issues, increased friction, and problems within the hydraulic system, especially affecting the controls for hydraulics and lever shifting for implement handling. As a result, operators may need
Soft-bending actuators have garnered significant interest in robotics and biomedical engineering due to their ability to mimic the bending motions of natural organisms. Using either positive or negative pressure, most soft pneumatic actuators for bending actuation have modified their design accordingly. In this study, we propose a novel soft bending actuator that utilizes combined positive and negative pressures to achieve enhanced performance and control. The actuator consists of a flexible elastomeric chamber divided into two compartments: a positive pressure chamber and a negative pressure chamber. Controlled bending motion can be achieved by selectively applying positive and negative pressures to the respective chambers. The combined positive and negative pressure allowed for faster response times and increased flexibility compared to traditional soft actuators. Because of its adaptability, controllability, and improved performance can be used for various jobs that call for careful
The parametrized twist beam suspension is a pivotal component in the automotive industry, profoundly influencing the ride comfort and handling characteristics of vehicles. This study presents a novel approach to optimizing twist beam suspension systems by leveraging parametric design principles. By introducing a parameter-driven framework, this research empowers engineers to systematically iterate and fine-tune twist beam designs, ultimately enhancing both ride quality and handling performance. The paper outlines the theoretical foundation of parametrized suspension design, emphasizing its significance in addressing the intricate balance between ride comfort and dynamic stability. Through a comprehensive examination of key suspension parameters, such as twist beam profile, material properties, and attachment points, the study demonstrates the versatility of the parametric approach in tailoring suspension characteristics to meet specific performance objectives. To validate the
Slosh, a phenomenon occurring in a vehicle's tank during movement, significantly contributes to noise and vibration, often exceeding idle levels. Existing methods for evaluating NVH performance of fuel tanks primarily rely on subjective assessment, highlighting the need for a quantifiable approach to address this dynamic noise. This paper introduces a hybrid methodology to standardize the slosh phenomenon by establishing vehicle-level acceleration, braking, and driving profiles. Noise and vibration data capture, combined with defined boundary conditions, categorizes slosh noise into Impact and Roll noise, differentiated by distinct driving profiles and frequency content. Vehicle level performance is then cascaded down to subsystem level. A dedicated test rig is designed that replicates these conditions at the subsystem level where vehicle speed and braking profiles are translated into rig-specific acceleration and deceleration profiles, enabling consistent data capture for correlation
Modal performance of a vehicle body often influences tactile vibrations felt by passengers as well as their acoustic comfort inside the cabin at low frequencies. This paper focuses on a premium hatchback’s development program where a design-intent initial batch of proto-cars were found to meet their targeted NVH performance. However, tactile vibrations in pre-production pilot batch vehicles were found to be of higher intensity. As a resolution, a method of cascading full vehicle level performance to its Body-In-White (BIW) component level was used to understand dynamic behavior of the vehicle and subsequently, to improve structural weakness of the body to achieve the targeted NVH performance. The cascaded modal performance indicated that global bending stiffness of the pre-production bodies was on the lower side w.r.t. that of the design intent body. To identify the root cause, design sensitivity of number and footprint of weld spots, roof bows’ and headers’ attachment stiffness to BIW
The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has significantly changed various industries. This study demonstrates the application of a Convolutional Neural Network (CNN) model in Computational Fluid Dynamics (CFD) to predict the drag coefficient of a complete vehicle profile. We have developed a design advisor that uses a custom 3D CNN with a U-net architecture in the DEP MeshWorks environment to predict drag coefficients (Cd) based on car shapes. This model understands the relationship between car shapes and air drag coefficients calculated using computational fluid dynamics (CFD). The AI/ML-based design advisor feature has the potential to significantly decrease the time required for predicting drag coefficients by conducting CFD calculations. During the initial development phase, it will serve as an efficient tool for analyzing the correlation between multiple design proposals and aerodynamic drag forces within a short time frame
Pick-and-place machines are a type of automated equipment used to place objects into structured, organized locations. These machines are used for a variety of applications — from electronics assembly to packaging, bin picking, and even inspection — but many current pick-and-place solutions are limited. Current solutions lack “precise generalization,” or the ability to solve many tasks without compromising on accuracy.
Reducing vehicle weight is a key task for automotive engineers to meet future emission, fuel consumption, and performance requirements. Weight reduction of cylinder head and crankcase can make a decisive contribution to achieving these objectives, as they are among the heaviest components of a passenger car powertrain. Modern passenger car cylinder heads and crankcases have greatly been optimized in terms of cost and weight in all-aluminum design using the latest conventional production techniques. However, it is becoming apparent that further significant weight reduction cannot be expected, as processes such as casting have reached their limits for further lightweighting due to manufacturing restrictions. Here, recent developments in the additive manufacturing (AM) of metallic structures is offering a new degree of freedom. As part of the government-funded research project LeiMot [Lightweight Engine (Eng.)] borderline lightweight design potential of a passenger car cylinder head with
Have you ever gazed at the vastness of the stars and wondered what else your CNC machine can create? Greg Green had the opportunity to find out when he joined the staff at the Canada-France-Hawaii Telescope (CFHT) in Waimea, Hawaii.
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