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Under various real-world scenarios, vehicles can become disabled and require towing. OEMs allow a few options for vehicle wrecker towing that include wheel lift tow using a stinger or towing on a flatbed. These methods entail multiple loading events that need to be assessed for damage to the towed vehicle. OEMs have several testing and evaluation methods in place for those scenarios with majority requiring physical vehicle prototypes. Recent focus to reduce product development time and cost has replaced the need for prototype testing with analytical verification methods. In this paper, the CAE method involving multibody dynamic simulation (MBDS) as well as finite element analysis (FEA) of vehicle flatbed operation, winching onto a flatbed, and stinger-pull towing are discussed.

A new methodology to predict vehicle interior wind noise using CFD results has been developed. The CFD simulation replaces wind tunnel testing for providing flow field information around vehicle greenhouse. A loadcase model based on the CFD results is used to excite an SEA vehicle model. This new approach has been demonstrated on a production vehicle with success for the frequency range of 250-10K Hz. The CAE prediction of interior wind noise agrees within 0.2 sones from wind tunnel testing. The model has been used to evaluate wind noise performance with different door glass design parameters. A glass thickness change from 3.8 mm to 4.8 mm results in 1.1 sones improvement, which agrees well to 1.4 sones improvement from testing. Laminated glass with about 3 times higher damping results in 2.5 sones improvement. This methodology using CFD results can be used in the early stage of product development to impact designs.

A key ingredient in designing products that are more robust is a thorough knowledge of the physics of the ideal function of those products and the physics of the failure modes of those products. We refer to the mathematical functions describing this physics as the transfer functions for that product. Dimensional analysis (DA) is a well known, but often overlooked, tool for reducing the number of experiments needed to characterize a physical system. In this paper, we demonstrate how the application of DA can be used to reduce the size of a DOE needed to estimate transfer functions experimentally. Furthermore, the transfer function generated using DOEs with DA tend to be more general than those generated using larger DOEs directly on the design parameters. With ever-increasing competitive pressure and reduced product development time, a tool such as DA, which can dramatically reduce experimental cost, is an incredibly valuable addition to an engineers toolbox.

The importance of the automotive test facility has increased significantly due in large part to continuous pressure on manufactures to shorten product development cycles. Test facilities are no longer used only for regulatory testing, or development testing in which the effects of small design changes (A-to-B testing) are determined; automotive manufacturers are beginning to use these facilities for final design validation, which has traditionally required on road testing. A host of resources have gone into the design and construction of facilities with the capability to simulate nearly any environment of practical importance to the automotive industry. As a result, there are now a number of test facilities, and specifically wind tunnels, in which engineers can test most aspects of a vehicle's performance in real-world environments.

Virtual Verification (VV) of engineering designs is a critical enabler in the Product Development (PD) process to reduce the time-to-market in a cost efficient manner. Reliance on cost effective VV methods have significantly increased with increased pressure to meet customer expectations for new products at reduced PD budgets. Computer Aided Engineering (CAE) is one such VV method that affords an engineer to make decisions about the ability of the designs to meet the design criteria even before a prototype is built. The first step of the CAE process is meshing which is a time consuming, manual and laborious process. Also mesh development time and accuracy significantly varies with the (1) component (trim body, engine, suspension, brakes, etc.), (2) features predominantly occurring in the component (welds, ribs, fillets, etc.), meshing guidelines based on which the model needs to be developed (durability, safety, NVH, etc.), and the expertise of the meshing engineer involved.

During the Product Development Process, the experimental engineers try to acquire the most reliable data from Proving Grounds early on the development process, aiming to support CAE model correlation and in this way ensuring that the vehicle is capable of withstanding customer loads. Those data, from Proving Grounds, are correlated to the most severe customer's usage and public road conditions. The proposal of this paper is to analyze how tire pressure affects structural durability, since safety, performance and fuel economy were already discussed on other opportunities. Tire pressure is important because it's one variable where the customer can monitor and act and because TPMS (Tire Pressure Measurement System) is not available on most vehicles sold in the Brazilian Market,

A hybrid electric vehicle (HEV) system model, which directly simulates vehicle drive cycles with interactions among driver, environment, vehicle hardware and vehicle controls, is a critical CAE tool used through out the product development process to project HEV fuel economy (FE) capabilities. The accuracy of the model is essential and directly influences the HEV hardware designs and technology decisions. This ultimately impacts HEV product content and cost. Therefore, improving HEV system model accuracy and establishing high-level model-test correlation are imperative. This paper presents a Parameter Diagram (P-Diagram) based model-test correlation framework which covers all areas contributing to potential model simulation vs. vehicle test differences. The paper describes each area in detail and the methods of characterizing the influences as well as the correlation metrics.

Automotive Product Development organisations are challenged with ever increasing levels of systems complexity driven by the introduction of new technologies to address environmental concerns and enhance customer satisfaction within a highly competitive and cost conscious market. The technical difficulty associated with the engineering of complex automotive systems is compounded by the increase in sophistication of the control systems needed to manage the integration of technology packages. Most automotive systems have an electro-mechanical structure with control and software features embedded within the system. The conventional methods for design analysis and synthesis are engineering discipline focused (mechanical, electrical, electronic, control, software).

In this paper, a systems engineering approach is explored to evaluate the effect of design parameters that contribute to the performance of the embedded Automatic Speech Recognition (ASR) engine in a vehicle. This includes vehicle designs that influence the presence of environmental and HVAC noise, microphone placement strategy, seat position, and cabin material and geometry. Interactions can be analyzed between these factors and dominant influencers identified. Relationships can then be established between ASR engine performance and attribute performance metrics that quantify the link between the two. This helps aid proper target setting and hardware selection to meet the customer satisfaction goals for both teams.

There are a multitude of dynamics faced by any industry. There is also a consistent search and development of technological platforms and services to address these changes. This necessitates a shared work philosophy which involves multiple stakeholders. Verification and validation are integral part of any development irrespective of product, process, or services. Also, every industry has a regulatory compliance to adhere too. But the extent of complexity and the level of dependencies or interactions between modules as well as stakeholders involved, creates slippage at some or other level. Nowadays the industries are also driven by reuse for cost effectiveness. Though it marks the significant improvement in the capability to compete, compatibility is a key measure to a successful product or service launch and sustainability.

There is a strong business case for automotive interfaces to undergo usability testing throughout their product development lifecycle. System Usability Scale (SUS) is a simple and standard measure of usability. To meet the timing needs for product development, usability testing needs to be performed in a quick, cost effective manner. Hence the required sample size of participants for a usability study is one of the critical factors. To determine an acceptable sample size, a Monte Carlo simulation using SUS scores from eleven different in-vehicle automotive interface usability studies was used to create 500,000 subsamples of different sample sizes. The percentage of subsamples with mean scores within the confidence interval of the population mean was calculated. At a subsample size of thirty-five, 95% of the subsamples have a mean SUS score within the 95% confidence interval of the population mean.

Driver assistance features are increasingly dependent upon system architectures that distribute and share responsibilities across various function-based ECUs to minimize cost and redundancy while maximizing engineering efficiency. Clear and accurate system requirements are critical to success, and a robust methodology for validating and testing requirements is essential. Distributed systems are highly sensitive to requirement ambiguity and inaccuracy as they are designed on the assumptions of predictable logical behavior of each functional component. Requirement ambiguity drives variance in implementations which results in system incompatibilities. Errors in requirements lead to faulty implementations that fail not just the component test but also hinder the testing of the entire system of components.

This paper focuses on the methodology development and application of reliability-based design optimization to a vehicle exhaust system under noise, vibration and harshness constraints with uncertainties. Reliability-based design optimization provides a systematic way for considering uncertainties in product development process. As traditional reliability analysis itself is a design optimization problem that requires many function evaluations, it often requires tremendous computational resources and efficient optimization methodologies. Multiple functional response constraints and large number of design variables add further complexity to the problem. This paper investigates an integrated approach by taking advantages of variable screening, design of experiments, response surface model, and reliability-based design optimization for problems with functional responses. A typical vehicle exhaust system is used as an example to demonstrate the methodology.

As automotive competition is becoming more and more global, automotive products (vehicles or systems or components) are often developed in one market and made and/or used in other markets. The operating conditions are often different from one market to another market. If the market unique operating conditions are not considered in its development and manufacture process, the product may not fully perform its intended function over useful life period, and may experience fewer failure modes in one market but more or different failure modes in other markets. This paper presents a Reliability Engineering approach, based on “Failure Mode Avoidance”, to the product development for global automotive markets. The approach includes three phases: Discover operating conditions (noise factors) and their potential failure modes Develop countermeasures to deal with the noise factors, and Verify the effectiveness of the countermeasures using Reliability Demonstration Matrix.

During the design conception of an automobile, typically low-fidelity physics-based simulations are coupled with engineering judgement to define key architectural components and subsystems which limits the capability to identify NVH issues arising from systems interaction. This translates to non-optimal designs because of unexplored design opportunities and therefore, lost business efficiencies. The sparse design information available during the design conception phase limits the development of representative higher fidelity physics-based simulations. To address that restriction on design optimization opportunities, this paper introduces an alternate approach to develop reduced order predictive models using regression techniques by harnessing historical measurement and simulation data. The concept is illustrated using two driveline NVH phenomenon: axle whine and take-off shudder.

Powertrain drivability evaluation and calibration is an important part of vehicle development to enhance the customer experience. This step mainly takes place on vehicle testing very late in the product development cycle, and is associated with a considerable amount of prototype, test facility, human resource and time cost. Design change options at this stage are also very limited. To reduce the development cost, a model based computer aided engineering (CAE) method is introduced and combined with hardware-in-the-loop (HIL) simulation technology. The HIL simulation method offers a possibility for drivability prediction and development in early phase of product cycle. This article describes the drivability HIL simulation process under development in Ford. The process consists of real time capable multi-domain CAE model integration, powertrain control module (PCM) and HIL simulator interface development and drivability HIL simulation.

Dr. Edwards Deming spirited organizations “If you can't define what you do as a process, you don't know what your job is” (Weinstein, 1999). Significant effort has been conducted to engineer, deploy and control a process to product development. This coverage reflects impact of product development process on developing and producing consumer products effectively and successfully for the future. Reflecting on the past and observing mistakes and lessons learned would be key to help our companies to engineer future or modify existing product development processes. This paper examines views, types and needs of product development process from a six sigma perspective to enable deliver of competitive products with cost and time in mind. Learning from the past enlightens us to identify opportunities that would drive evolution and trend of product development process into the future. A recommended view is presented that changes the way product development process is designed and implemented.

One of the biggest challenges for the Product Development Engineers is to have a clear understanding of the Quality Principles and Disciplines they should follow while they are engineering. In general, the current Product Development System guides of the Automakers companies are mostly focused on provide guidance for the Engineers on the following areas: Design Efficiency; Design Rules for Product Robustness; Design Validation; Product Reliability; Testing Procedures. The introduction of a new/advanced technology system alone does not mean low incidence of customer complaints. The only way to get that is plan/execute Consumer Driven Design with excellence. Global Vehicles are more sensitive to Quality since they must satisfy diverse cultural customers without compromise reliability. When a new vehicle is being developed to be sold in many markets around the world - Global Product - this problem is even bigger. Different markets mean different customer expectations.

The highly competitive auto industry is looking for ways to reduce product development cycle time while meeting the corporate and government stringent vehicle performance requirements. Quasi-static or dynamic analytical fatigue life assessment of automotive structures consumes more time because of the use of long proving ground time histories and large finite element models with more than a million elements. A representative static load case that highlights all the durability concern locations is needed for making fast design iterations. This paper describes a simple technique to extract a static load case that correlates to minimum fatigue life at various locations of the body structure and the method of using this load case for fast iterations before validating the final design with fatigue analysis using full proving ground loads. The usefulness of this static load case in solving sheet metal and spot weld fatigue issues is demonstrated with an example.

The design discipline of Design For Six Sigma (DFSS) has been applied to many areas of product development and manufacturing. As DFSS application has recently been extended to upfront automotive engineering areas such as research and advanced development, more robust and optimized technologies can be achieved in the pre-production stage, reducing cost, exhibiting superior quality and performance, and shortened development cycle. This paper describes the application of the DFSS process, Define, Characterize, Optimize, and Verify (DCOV) to develop an automotive technology that begins from the conceptual phase and continues up through the implementation phase. The role of DFSS in the automotive industry is to provide a framework for more rigorous upfront engineering. It provides guidelines to a more effective and efficient development of new technologies.