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Regulations implemented by safety commissions throughout the world have resulted in extensive physical testing to protect the occupants during frontal impact events. Significant prototype and test costs aimed at optimizing structure and restraint systems are associated with meeting these regulations. To help reduce development costs, Computer Aided Engineering (CAE) is often applied. LS-DYNA [1] coupled with MADYMO [2] is widely used in crash and occupant safety simulation. An analysis technique which utilized a single model to design and optimize interiors (instrument panel, seats, visor, steering wheel, steering column) and restraints (airbag, seatbelts, retractor, pre-tensioner) was developed. The single model concept captures the global structural kinematics through minimal vehicle representation. Global vehicle modes such as pitch and roll can be represented by applying prescribed motion boundary conditions extracted from full vehicle models.

Digital engineering has been utilized in product development to improve the quality. The actual object was measured and digitized into the three-dimensional (3-D) data, and the requirement of evaluating and analyzing the CAD data has been increased in these activities. So, we developed the technology that measures the actual object and obtains the 3-D model data for general automotive parts. The features of this new system are high-speed and high-accuracy by using high energy X-ray CT technology and 3-D model data technology. 3-D model data can be obtained for about 5 hours in case of the engine block and the error is 0.1mm or less. We also show the examples of the new automotive parts development using this technology.

In an evaluation of crashworthiness for the cars made of aluminum alloys, the evaluation considering fracture phenomenon comes to be needed because conventional aluminum alloys have low fracture strain (10-20%). In case of the development of a B-Pillar made by die cast, if crack occurrence, furthermore, separation of a part can be estimated by using CAE in crashworthiness evaluations, we can reduce the number of prototype makings and the cost of development using expensive dies. Therefore, we performed crashworthiness evaluations by CAE using some sort of a damage & fracture material model. It is known as “Orthotropic damage & fracture model”.

Springback study on a Dodge Ram fender outer panel is detailed in this paper. A simple measurement fixture is designed for the panel, wherein non-contact laser scan technology is applied The measurement data are compared with the original CAD design surface and deviation contour maps are obtained. Consistency of measurement is studied at different sections among three samples. Details of FEA simulations are outlined. The comparison between measurement and simulation prediction is summarized. A method to describe the consistency of measurement and the accuracy of simulation prediction is proposed. The targets for measurement consistency and simulation accuracy are verified. A sensitivity analysis is also performed to investigate various simulation input parameters.

Today automotive thermal systems development is a joint effort between an OEM and its suppliers. This paper presents a pilot program showing how OEMs and suppliers can jointly develop a reliable and robust thermal system using CAE tools over the internet. Federated Intelligent Product Environment (FIPER) has been used to establish B2B communication between OEMs and suppliers. Suppliers remotely run thermal systems computer models at the OEM site using the FIPER B2B feature.

A process to achieve vehicle system level NVH objectives using CAE simulation tools is discussed. Issues of modeling methodology, already covered adequately in the literature, are less emphasized so that the paper can focus on the application of a process that encompasses objective setting, design synthesis, and performance achievement using simulation predictions. A reference simulation model establishes correlation levels and modeling methods that are applied to future predictions. The new model, called a “Digital Mule”, is an early new product “design intent” simulation used to arrive at subsystem goals to meet the vehicle level NVH objectives. Subsystem goals are established at discrete noise paths where structure borne noise enters the body subsystem. The process also includes setting limits on the excitation sources, such as suspension and powertrain.

In automotive industry, all passenger vehicles are treated with damping materials to reduce structure borne noise. The effectiveness of damping treatments depends upon design parameters such as choice of damping materials, locations and size of the treatment. This paper proposes a CAE (Computer Aided Engineering) methodology based on finite element analysis to optimize damping treatments. The developed method uses modal strain-energy information of bare structural panels to identify flexible regions, which in turn facilitates optimization of damping treatments with respect to location and size. The efficacy of the method is demonstrated by optimizing damping treatment for a full-size pick-up truck. Moreover, simulated road noise performances of the truck with and without damping treatments are compared, which show the benefits of applying damping treatment.

When developing an off-road vehicle, it is essential to create excellent drivability that enables the vehicle to be driven on all surfaces while ensuring passenger comfort. Since durability is another indispensable performance aspect for these vehicles, the development method must be capable of considering a high-level combination of a wide range of performance targets. This paper proposes a method to identify the region in which each performance aspect is realized through a complex domain combination problem. The proposed method is helpful in the initial design stage when the detailed specifications of the target vehicle are not determined because it is capable of considering both the specifications and usage method of the target vehicle, such as the selection of road profiles and driving speeds as design variables. The proposed method has the advantage of enabling efficient concurrent studies to search for feasible regions.

In the early stages of a vehicle program, it is a common practice to set target ranges for the global body, suspension and powertrain modes. This modal management process allows engineers to avoid potential noise and vibration problems stemming from strong overlap of major global modes. Before the first prototype hardware is built, finite element models of the body, suspension and powertrain are usually exercised to compare predicted versus targeted ranges of the major system modes in the form of a modal management chart. However, uncertainty associated with the design parameters, manufacturing process and other sources can lead to a major departure from the design intent when the first hardware prototype is built. In this study, a first order reliability method is used to predict variance of the eigen values due to parameter uncertainties. This allows the CAE engineers to add a “three sigma” bound on the eigen values reported in the modal management chart.

This paper describes a lightweight magnesium intensive automobile body structure concept developed at DaimlerChrysler to support a high fuel-efficiency vehicle project. This body structure resulted in more than 40% weight reduction over a conventional steel structure while achieving significantly improved structural performance as evaluated through CAE simulations. A business case analysis was conducted and showed promising results. One concept vehicle was built for the purpose of demonstrating concept feasibility. The paper also identifies areas for further development to enable such a vehicle to become a production reality at a later time.

Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.

To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against ground impact caused by road debris is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE model. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise direction, magnitude, and location of the force applied from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian process regression (GPR), to perform inverse analysis of the direction, magnitude, and location of vehicle-road contact forces during rough road conditions.

Exhaust durability is an important measure of quality, which can be predicted using CAE with accurate mount loads. This paper proposes an innovative method to calculate these loads from measured mount accelerations. A Chrysler vehicle was instrumented with accelerometers at both ends of its four exhaust mounts. The vehicle was tested at various durability routes or events at DaimlerChrysler Proving Grounds. These measured accelerations were integrated to obtain their velocities and displacements. The differences in velocities and displacements at each mount were multiplied by its damping and stiffness rates to obtain the mount load. The calculation was conducted for all three translational directions and for all events. The calculated mount loads are shown within reasonable range. Along with CAE, it is suggested to explore this method for exhaust durability development.

Knowledge of experts is necessary for judging motor current waveforms. Here, we develop an automatic judgement system for motor current waveform by establishing an AI model trained by knowledge of experts and CAE technology.

Current vehicle acoustic performance prediction methods, CAE (computer aided engineering) or physical testing, have some difficulty predicting interior sound in the mid-frequency range (300 to 1000 Hz). It is in this frequency range where the overall acoustic performance becomes sensitive to not only the contributions of structure-borne sources, which can be studied using traditional finite element analysis (FEA) methods, but also the contribution of airborne noise sources which increase proportional to frequency. It is in this higher frequency range (>1000 Hz) that physical testing and statistical CAE methods are traditionally used for performance studies. This paper will discuss a study that was undertaken to test the capability of a finite element modeling method that can accurately simulate air-borne noise phenomena in the mid-frequency range.

In recent years, improving the engine thermal efficiency is strongly required. To enhance the engine thermal efficiency, it is important to improve the engine anti-knock quality. Technologies for modifying engine cooling have been developed to improve anti-knocking quality of engines. However, excessive improvement of engine cooling leads to an increase in cooling heat loss. Therefore, it is necessary to clarify the effects of the temperature of each part of the engine such as engine head-cylinder, cylinder-liner, and piston on knocking and cooling heat loss. In this paper, computer aided engineering (CAE) is used to predict the effects of each part of the engine on engine knocking and cooling heat loss. Firstly, the amount of heat energy that air-fuel mixture receives from engine cylinder-head, cylinder-liner, and piston is calculated during the intake stroke. The result shows that the cylinder-liner contributes largest heat energy to air-fuel mixture, especially the exhaust side.

Computer Aided Engineering system for automotive piston design was developed which can predict total piston performance in a short time at the planning stage of piston design. Many previous studies attempted to calculate piston performance accurately with experimental data and their main purpose was not to create a tool for piston design. The purpose of this CAE system is to provide a tool for a designer to predict total piston performance easily and rapidly without experimental data. This system has following two characteristics. Firstly, new finite element methods were developed which can predict temperature distribution without experimental data, thermal skirt expansion for a strutted piston and skirt-to-bore contact pressure under engine operating conditions. The predicted result are accurate enough to predict piston performance at the planning stage of piston design.

Vehicle design and packaging is a repetitive and tedious process that involves frequent engineering and design changes. To improve design efficiency, a standard naming vehicle design methodology is proposed in this paper. For the geometric or the functional object used in the vehicle context, a standard name is assigned and also used as a unique object feature through its life cycle. With the proposed standard naming design methodology, the engineering knowledge can be efficiently embedded into the CAD design, and hence, vehicle design can be executed in a more automated fashion. Work case of the standard naming design methodology is illustrated by a vehicle design and packaging application using CATIA V5.

Computer applications have been widely used to assist product design. The successes and sophistication of computer aided engineering (CAE) techniques are respectfully recognized in this field. CAE applications in the manufacturing area however are still developing, although the manufacturing community is increasingly starting to pay attentions to computer simulations in its daily workings. This paper will briefly introduce some of these applications and promote awareness of computer simulations in manufacturing area. It contains four main sections: finite element analysis (FEA) in machining fixture design, FEA applications in component assembly, machining process simulations and machining vibrations in the milling operation. Each section comes with a practical case study, potential benefits are identified and conclusions are presented by using an integrated design and analysis approach.

Since the regulations of CO2 emissions have been tightened in each country recently, each automotive manufacturer has responded by bringing competitive technologies that maximize efficiency while promoting vehicle electrification such as xEV. Not only the efficiency, we need to meet or exceed occupant performance and comfort expectations. The climate control system expends a large amount of energy to keep a comfortable environment, having a significant impact on fuel consumption and EV driving range. Therefore, many manufacturers try to save energy and improve occupant comfort quickly by using not only the conventional convective heating by HVAC but also the conductive heating to heat the human body directly such as seat and steering wheel heater. In this study, a radiative heater, which is more efficient than a convective heating to warm anterior thigh and shin where a conductive heating cannot warm, was applied to vehicle.