Browse Topic: CAD, CAM, and CAE
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
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
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
ABSTRACT The objective of this effort is to create a parametric Computer-Aided Design (CAD) accommodation model for the Fixed Heel Point (FHP) driver and crew workstations with specific tasks. The FHP model is a statistical model that was created utilizing data from the Seated Soldier Study (Reed and Ebert, 2013). The final product is a stand-alone CAD model that provides geometric boundaries indicating the required space and adjustments needed for the equipped Soldiers’ helmet, eyes, torso, knees, and seat travel. Clearances between the Soldier and surrounding interior surfaces and direct field of view have been added per MIL-STD-1472G. This CAD model can be applied early in the vehicle design process to ensure accommodation requirements are met and help explore possible design tradeoffs when conflicts with other design parameters exist. The CAD model will be available once it has undergone Verification, Validation, and Accreditation (VV&A) and a user guide has been written
ABSTRACT Conceptual design of automotive structures has received substantial research attention in recent years in order to speed up vehicle development and innovation. Although several structural optimization methods have been employed in concept design, there still exists lack of efficient design tools to produce initial design shapes with less problem dependency, less computation-intensive analysis and more design flexibility. In this paper, an innovative Computer Aided Engineering (CAE) approach based on an integrated Genetic Algorithms(GA) and Finite Element (FE) optimization system has been studied and implemented for efficient conceptual design of automotive suspension system related structural part. Integration of GA provides the method a great amount of design flexibility and robustness that increases possibility of finding more efficient and innovative design shapes of the structure
ABSTRACT Gas metal arc pulse directed energy deposition (GMA-P DED) offers large-scale additive manufacturing (AM) capabilities and lower cost systems compared to laser or electron beam DED. These advantages position GMA-DED as a promising manufacturing process for widespread industrial adoption. To enable this “digital” manufacturing of a component from a computer-aided design (CAD) file, a computer-aided manufacturing (CAM) solver is necessary to generate build plans and utilize welding parameter sets based on feature and application requirements. Scalable and robot-agnostic computer-aided robotics (CAR) software is therefore essential to provide automated toolpath generation. This work establishes the use of Autodesk PowerMill Ultimate software as a CAM/CAR solution for arc-based DED processes across robot manufacturers. Preferred aluminum GMA-P DED welding parameters were developed for single-pass wide “walls” and multi-pass wide “blocks” that can be configured to build a wide
ABSTRACT Application of human figure modeling tools and techniques has proven to be a valuable asset in the effort to examine man-machine interface problems through the evaluation of 3D CAD models of workspace designs. Digital human figure modeling has also become a key tool to help ensure that Human Systems Integration (HSI) requirements are met for US Army weapon systems and platforms. However, challenges still exist to the effective application of human figure modeling especially with regard to military platforms. For example, any accommodation analysis of these systems must not only account for the physical dimensions of the target Soldier population but also the specialized mission clothing and equipment such as body armor, hydration packs, extreme cold weather gear and chemical protective equipment to name just a few. Other design aspects such as seating, blast mitigation components, controls and communication equipment are often unique to military platforms and present special
ABSTRACT As part of DARPA’s Adaptive Vehicle Make (AVM) portfolio of programs, blast and ballistic survivability analysis tools were developed. The intent of these tools was to facilitate design and design optimization by making it possible for designers to perform survivability analysis from CAD and to automate the survivability analysis pipeline to allow optimization codes to invoke the survivability tools and obtain results. This paper describes some of the tools and their capabilities through highlighting five innovations utilized in the program: multi-fidelity modeling; automated meshing and welding; uncertainty quantification and 95% bounds; a large material property database and more accurate blast loads; and automating the entire computational pipeline
ABSTRACT An endgame, vulnerability/lethality code, TurboPK was developed to take advantage of parallel processing of multi-core, modern-day desktop and laptop computers. TurboPK is used to simulate and analyze weapon-related kinetic energy and blast effects of military vehicles. It implements Department of Defense (DoD)-approved algorithms and is compatible with the DoD design trade-off process. Its speed advantage is commensurate with the increase in number of cores used. A quad-core processor results in run times that are four times faster than using a single core. The heart of endgame analysis calculates geometric intersections of projectiles or fragments with vehicle components using ray-tracing algorithms. For example, literally thousands of rays are used to accurately model the fragment ejecta from a warhead in a burst point analysis. Algorithms originally written for a single processor have been rewritten to exploit an open-source, parallel process ray tracer called Embree
ABSTRACT Probabilistic Principal Component Analysis (PPCA) is a promising tool for validating tests and computational models by means of comparing the multivariate time histories they generate to available field data. Following PPCA by interval-based Bayesian hypothesis testing enables acceptance or rejection of the tests and models given the available field data. In this work, we investigate the robustness of this methodology and present sensitivity studies of validating hybrid powertrain models of a military vehicle simulated over different proving ground courses
ABSTRACT The University of Delaware (UD) and the US Army DEVCOM-GVSC (GVSC) have partnered to show the feasibility of fabricating mission specific, man-packable, autonomous vehicles that are created by Computer Aided Design (CAD) and are then produced, from start-to-finish, in a single manufacturing unit-cell without human intervention in the manufacturing process. This unit-cell contains many manufacturing processes (e.g., additive manufacturing (AM), pick-and-place, circuit printing, and subtractive manufacturing) that work in concert to fabricate functional devices. Together, UD and GVSC have developed the very first mission specific autonomous vehicle that is fully fabricated in a single manufacturing unit-cell without being touched by human hand. Citation: Jacob W. Robinson, Thomas W. Lum, Zachary J. Larimore, Matthew P. Ludkey, Larry (LJ) R. Holmes, Jr. “AUTOMATED MANUFACTURING FOR AUTONOMOUS SYSTEMS SOLUTIONS (AMASS)”, In Proceedings of the Ground Vehicle Systems Engineering and
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
Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined. Absolute and
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