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The design and development of electric vehicles involves many unique challenges. One such challenge involves accurately predicting driveline abuse torque loads early in the design cycle to aid with sizing drive-unit and driveline components. Since electrified drivelines typically lack a torque-limiting “fuse” element such as a torque converter or slipping clutch, they can be vulnerable to sudden transient events involving high wheel acceleration or deceleration. Component sizing must account for the loads caused by such events, and these loads must be accurately quantified early on when vehicle parameters haven’t been finalized yet. Early load predictions can be made by completing abuse maneuver simulations where key parameters are varied to gauge their influence on simulated loads. Understanding how these parameters impact loads allows for better risk assessment during the design process, as these parameters will inevitably change until a final design is iterated upon.

This study involves an application of Principal Component Analysis (PCA) conducted in support of a Design for Six Sigma (DFSS) project. Primary focus of the project is to optimize seat parameters that influence Low Speed Rear Impact (LSRI) whiplash performance. During the DFSS study, the project team identified a need to rank order critical design factors statistically and establish their contribution to LSRI performance. It is also required to develop a transfer function for the LSRI rating in terms of test response parameters that can be used for optimization. This statistical approach resulted in a reliable transfer function that can applied across all seat designs and enabled us to separate vital few parameters from several many.

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.

The present study defines the functional requirements for a liftgate and the body in order to avoid rattle, squeak, and other objectionable noises. A Design For Six Sigma (DFSS) methodology was used to study the impact of various constraint components such as bumpers, wedges, and isolated strikers on functional requirements. These functional requirements include liftgate frequency, acoustic cavity frequency, and the stiffness of the liftgate body opening. It has been determined that the method of constraining the gate relative to the body opening has a strong correlation to the noise generated. The recommended functional performance targets and constraint component selection have been confirmed by actual testing on a vehicle. Recommendations for future liftgate design will be presented.

The Hood ajar sensing system provides customer feedback regarding the latch positional state of hood. If the sensing system is not robust to variation due to manufacturing, thermal conditions, and assembly, diagnostic failures can result. Executing various elements of the design for six sigma process can reduce the potential for diagnostic failures. This paper presents a method for achieving quality improvements by developing transfer functions, and using them for sensitivity and variance analysis. Control parameters were optimized to minimize non-conformal situations in the presence of various noise conditions.

Developing a robust model that can simulate all real world conditions a vehicle can experience can be extremely difficult to predict. When working through the engineering process, Computer Aided Engineers (CAE) traditionally set modeling parameters and conditions to a nominal setting. This is done to simplify the models so that it avoided inputting too much tedious details into the system and wasting so much engineering time preparing the work. It was soon realized that this strategy did not capture all the possible conditions a hood on a vehicle could experience. There was a need to develop a formal approach and method to correlate an analysis model to real world conditions. The Design for Six Sigma (DFSS) process was utilized to develop robustness in the techniques used to accurately understand the vehicle environment. The DFSS process is normally used to design and develop robustness into physical parts.

Roof racks are designed for carrying luggage during customers' travels. These rails need to be strong enough to be able to carry the luggage weight as well as be able to withstand aerodynamic loads that are generated when the vehicle is travelling at high speeds on highways. Traditionally, roof rail gage thickness is increased to account for these load cases (since these are manufactured by extrusion), but doing so leads to increased mass which adversely affects fuel efficiency. The current study focuses on providing the guidelines for strategically placing lightening holes and optimizing gage thickness so that the final design is robust to noise parameters and saves the most mass without adversely impacting wind noise performance while minimizing stress. The project applied Design for Six Sigma (DFSS) techniques to optimize roof rail parameters in order to improve the load carrying capacity while minimizing mass.

Robust engineering is an integral part of the quality initiative, Design For Six Sigma (DFSS), in most companies to enable good designs and products for reliability and durability. Taguchi’s signal-to-noise ratio has been considered as a good performance index for robustness for many years. An alternate approach that is direct and simple for measuring robustness is proposed. In this approach, robustness is measured in terms of an augmented output response and it is a composite index of variation and efficiency of a system. This formulation represents an engineering design intent of a product in a statistical sense, so engineers can understand, communicate, and resonate at ease. Robust formulations are illustrated and discussed with case studies for smaller-the-better, nominal-the-best, and dynamic responses. Confirmation runs of optimization show good agreement of the augmented response with the additive predictive models.