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Experimental decklids for the Cadillac STS sedan were made with Al AA5083 sheet outer panels and Mg AZ31B sheet inner panels using regular-production forming processes and hardware. Joining and coating processes were developed to accommodate the unique properties of Mg. Assembled decklids were evaluated for dimensional accuracy, slam durability, and impact response. The assemblies performed very well in these tests. Explicit and implicit finite element simulations of decklids were conducted, and showed that the Al/Mg decklids have good stiffness and strength characteristics. These results suggest the feasibility of using Mg sheet closure panels from a structural perspective.

The performance and durability of the lead acid battery is highly dependent on the internal battery temperature. The changes in internal battery temperatures are caused by several factors including internal heat generation and external heat transfer from the vehicle under-hood environment. Internal heat generation depends on the battery charging strategy and electric loading. External heat transfer effects are caused by customer duty cycle, vehicle under-hood components and under-hood ambient air. During soak conditions, the ambient temperature can have significant effect on battery temperature after a long drive for example. Therefore, the temperature rise in a lead-acid battery must be controlled to improve its performance and durability. In this paper a thermal model for lead-acid battery is developed which integrates both internal and external factors along with customer duty cycle to predict battery temperature at various driving conditions.

In this paper we present an integrated approach which combines analysis of the effect of simultaneous variations in model input parameters on component or system temperatures. The sensitivity analysis can be conducted by varying model input parameters using specific values that may be of interest to the user. The alternative approach is to use a structured set of parameters generated in the form of a DFSS DOE matrix. The matrix represents a combination of simulation conditions which combine the control factors (CF) and noise factors. CF’s are the design parameters that the engineer can modify to achieve a robust design. Noise factors include parameters that are outside the control of the design engineer. In automotive thermal management, noise factors include changes in ambient temperature, exhaust gas temperatures or aging of exhaust system or heat shields for example.

Automotive companies are studying to add extra value in their vehicles by enhancing powertrain sound quality. The objective is to create a brand sound that is unique and preferred by their customers since quietness is not always the most desired characteristic, especially for high-performance products. This paper describes the process of developing a brand powertrain sound for a high-performance vehicle using the DFSS methodology. Initially the customer's preferred sound was identified and analyzed. This was achieved by subjective evaluations through voice-of-customer clinics using vehicles of similar specifications. Objective data were acquired during several driving conditions. In order for the design process to be effective, it is very important to understand the relationship between subjective results and physical quantities of sound. Several sound quality metrics were calculated during the data analysis process.

Automobiles have a high degree of mechanical and electrical complexity. However, product complexity has the accompanying effect of requiring high levels of design and process oversight. The net result is a product creation process which is prone to creating failures. These failures typically have their origin in an overall lack of complete understanding of the system in terms of materials, geometries and energy flows. Despite all of the engineering intentions, failures are inevitable, common, and must be dealt with accordingly. In the worst case, if a failure manifests itself into an observable failure the customer may have a negative experience. Therefore, it is imperative that design engineers, suppliers along with reliability professionals be able to assess the design risk. One approach to assess risk is the use of degradation analysis. Degradation analysis often provides more information than failure time data for assessing reliability and predicting the remnant life of a system.

A cargo box is a key structure in a pickup truck which is used to hold various items. Therefore, a cargo box must be durable and robust under different ballast conditions when subjected to road load inputs. This paper discusses a Design for Six Sigma (DFSS) approach to improve the durability of cargo box panel in its early development phase. Traditional methods and best practices resulted in multiple iterations without an obvious solution. Hence, DFSS tools were proposed to find a robust and optimum solution. Key control factors/design parameters were identified, and L18 Orthogonal Array was chosen to optimize design using CAE tools. The optimum design selected was the one with the minimum stress level and the least stress variation. This design was confirmed to have significant improvement and robustness compared to the initial design. DFSS identified load paths which helped teams finally come up with integrated shear plate to resolve the durability concern.

An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers.

It is a challenging task to find an optimal design concept for a truck frame structure given the complexity of loading conditions, vehicle configurations, packaging and other requirements. In addition, there is a great emphasis on light weight frame design to meet stringent emission standards. This paper provides a framework for fast and efficient development of a frame structure through various design phases, keeping durability in perspective while utilizing various weight reduction techniques. In this approach frame weight and stiffness are optimized to meet strength and durability performance requirements. Fast evaluation of different frame configurations during the concept phase (I) was made possible by using DFSS (Design for Six Sigma) based system synthesis techniques. This resulted in a very efficient frame ladder concept selection process.

Understanding the flow characteristics and, especially, how the aerodynamic forces are influenced by the changes in the vehicle body shape, are very important in order to improve vehicle aerodynamics. One specific goal of aerodynamic shape optimization is to predict the local shape sensitivities for aerodynamic forces. The availability of a reliable and efficient sensitivity analysis method will help to reduce the number of design iterations and the aerodynamic development costs. Among various shape optimization methods, the Adjoint Method has received much attention as an efficient sensitivity analysis method for aerodynamic shape optimization because it allows the computation of sensitivity information for a large number of shape parameters simultaneously.

In today's light-weight vehicles, the strength of spot welds plays an important role in overall product integrity, reliability and customer satisfaction. Naturally, there is a need for a quick and reliable technique to inspect the quality of the welds. In the past, the primary quality control tests for detecting weld defects are the destructive chisel test and peel test [1]. The non-destructive evaluation (NDE) method currently used in industry is based on ultrasonic inspection [2, 3, 4]. The technique is not always successful in evaluating the nugget size, nor is it effective in detecting the so-called “cold” or “stick” welds. Therefore, it is necessary to develop a precise and reliable noncontact NDE method for spot welds. There have been numerous studies in predicting the weld nugget size by considering the spot-weld process [5, 6].

In 2006, the Insurance Institute for Highway Safety (IIHS) released a new Low Speed Bumper Test Protocol for passenger cars1. The new test protocol included the development of a deformable barrier that the vehicle would impact at low speeds. IIHS positioned the new barrier to improve correlation to low speed collisions in the field, and also to assess the ability of the bumper system to protect the vehicle from damage. The bumper system must stay engaged to the barrier to protect other vehicle components from damage. The challenge is to identify the bumper system design features that minimize additional cost and mass to keep engagement to the barrier. The results of the Design for Six Sigma analysis identified the design features that increase the stiffness of the bumper system enable it to stay engaged to the barrier and reduce the deflection.

Nowadays development of automotive HVAC is a challenging task wherein thermal comfort and safety are very critical factors to be met. HVAC system is responsible for the demisting and defrosting of the vehicle’s windshield and for creating/maintaining a pleasing environment inside the cabin by controlling airflow, velocity, temperature and purity of air. Fog or ice which forms on the windshield is the main reason for invisibility and leads to major safety issues to the customers while driving. It has been shown that proper clear visibility for the windshield could be obtained with a better flow pattern and uniform flow distribution in the defrost mode of the HVAC system and defrost duct. Defroster performance has received significant attention from OEMs to meet the specific global performance standards of FMVSS103 and SAE J902. Therefore, defroster performance is seriously taken into consideration during the design of HVAC system and defroster duct.

Geometry Tree is a term describing the product assembly structure and the manufacturing process for the product. The concept refers to the assembly structure of the final vehicle (the Part Tree) and the assembly process and tools for the final product (the Process Tree). In the past few years, the Geometry Tree-based quality process was piloted in the FCA US LLC assembly plants and has since evolved into a standardized quality control process. In the Part Tree process, the coordinated measurements and naming convention are enforced throughout the different levels of detailed products to sub-assemblies and measurement processes. The Process Tree, on the other hand, includes both prominently identified assembly tools and the mapping of key product characteristics to key assembly tools. The benefits of directly tying critical customer characteristics to actual machine components that have a high propensity to influence them is both preventive and reactive.

Vehicle suspension parts are subjected to variable road loads, manufacturing process variation and high installation loads in assembly process. These parts must be robust to usage conditions to function properly in the field. Design for Six Sigma (DFSS) tools and Taguchi Method were used to optimize initial rear suspension trailing arm design. Project identified key control factor/design parameters, to improve part robustness at the lowest cost. Optimized design performs well under higher road loads and meets stringent durability requirements. This paper evokes use of Taguchi Method to design robust rear suspension trailing arm and study effect of selected design parameters on robustness, stress level/durability and part cost.

Global programs are placing demands on vehicle platforms to achieve structural durability robustness across a broader spectrum of vehicle configurations and use conditions. This robustness is optimally achieved by (a) localizing energy absorption to lower cost components, and (b) narrowing the spread in loads generated during durability events, which in turn minimizes the cost and mass impact to the vehicle platform. A generalized philosophy for conducting load optimization and for improving energy management for various types of events is presented here. Various techniques that have been employed at GM are explained by way of illustration.

This study documents a method developed for dynamically measuring occupant pocketing during various low-speed rear impact, or “whiplash” sled tests. This dynamic pocketing measurement can then be related to the various test parameters used to establish the performance rating or compliance results. Consumer metric and regulatory tests discussed within this paper as potential applications of this technique include, but are not limited to, the Insurance Institute for Highway Safety (IIHS) Low Speed Rear Impact (LSRI) rating, Federal Motor Vehicle Safety Standard (FMVSS) 202a, and European New Car Assessment Program (EURO-NCAP) whiplash rating. Example metrics are also described which may be used to assist in establishing the design position of the head restraint and optimize the balance between low-speed rear impact performance and customer comfort.

For decades, the industry standard for laboratory durability simulations has been based on reproducing quantified vehicle responses. That is, build a running vehicle, measure its responses over a variety of durability road surfaces and reproduce those responses in the laboratory for durability evaluation. To bring a vehicle to market quickly, the time between tightening the last bolt on a prototype test vehicle and starting the durability evaluation test must be minimized. A method to derive 4-Post simulator displacements without measuring or predicting vehicle responses is presented.

Engineering has continuously strived to improve the vehicle development process to achieve high quality designs and quick to launch products. The design process has to have the tools and capabilities to help ensure both quick to the market product and a flawless launch. To achieve high fidelity and robust design, mistakes and other quality issues must be addressed early in the engineering process. One way to detect problems early is to use the math based modeling and simulation techniques of the analysis group. The correlation of the actual vehicle performance to the predictive model is crucial to obtain. Without high correlation, the change management process begins to get complicated and costs start to increase exponentially. It is critical to reduce and eliminate the risk in a design up front before tooling begins to kick off. The push to help achieve a high rate of correlation has been initiated by engineering management, seeing this as an asset to the business.

Current manufacture of alternative energy sources for automobiles, such as fuel cells and lithium-ion batteries, uses repeating energy modules to achieve targeted balances of power and weight for varying types of vehicles. Specifically for lithium-ion batteries, tens to hundreds of identical plastic parts are assembled in a repeating fashion; this assembly of parts requires complex dimensional planning and high degrees of quality control. This paper will address the aspects of dimensional quality for repeated, injection molded thermoplastic battery components and will include the following: First, dimensional variation associated with thermoplastic components is considered. Sources of variation include the injection molding process, tooling or mold, lot-to-lot material differences, and varying types of environmental exposure. Second, mold tuning and cavity matching between molds for multi-cavity production will be analyzed.

For many automotive systems it is required to calculate both the durability performance of the part and to rule out the possibility of collision of individual components during severe base shake vibration conditions. Advanced frequency domain methods now exist to enable the durability assessment to be undertaken fully in the frequency domain and utilizing the most advanced and efficient analysis tools (refs 1, 2, 3, 4, 5). In recent years new capabilities have been developed which allow hyper-sized models with multiple correlated loadcases to be processed. The most advanced stress processing (eg, complex von-Mises) and fatigue algorithms (eg, Strain-Life) are now included. Furthermore, the previously required assumptions that the loading be stationary, Gaussian and random have been somewhat relaxed. For example, mixed loading like sine on random can now be applied.