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The Chevrolet Volt is an electric vehicle with extended-range that is capable of operation on battery power alone, and on engine power after depletion of the battery charge. First generation Chevrolet Volts were driven over half a billion miles in North America from October 2013 through September 2014, 74% of which were all-electric [1, 12]. For 2016, GM has developed the second-generation of the Volt vehicle and “Voltec” propulsion system. By significantly re-engineering the traction power inverter module (TPIM) for the second-generation Chevrolet Volt extended-range electric vehicle (EREV), we were able to meet all performance targets while maintaining extremely high reliability and environmental robustness. The power switch was re-designed to achieve efficiency targets and meet thermal challenges. A novel cooling approach enables high power density while maintaining a very high overall conversion efficiency.

This paper proposes a novel probabilistic approach for multidisciplinary design optimization (MDO) under uncertainty, especially for systems with feedback coupled analyses with multiple coupling variables. The proposed approach consists of four components: multidisciplinary analysis, Bayesian network, copula-based sampling, and design optimization. The Bayesian network represents the joint distribution of multiple variables through marginal distributions and conditional probabilities, and updates the distributions based on new data. In this methodology, the Bayesian network is pursued in two directions: (1) probabilistic surrogate modeling to estimate the output uncertainty given values of the design variables, and (2) probabilistic multidisciplinary analysis (MDA) to infer the distributions of the coupling and output variables that satisfy interdisciplinary compatibility conditions.

Reliability-based design optimization (RBDO) has been widely used to obtain a reliable design via an existing CAE model considering the variations of input variables. However, most RBDO approaches do not consider the CAE model bias and uncertainty, which may largely affect the reliability assessment of the final design and result in risky design decisions. In this paper, the Gaussian Process Modeling (GPM) approach is applied to statistically correct the model discrepancy which is represented as a bias function, and to quantify model uncertainty based on collected data from either real tests or high-fidelity CAE simulations. After the corrected model is validated by extra sets of test data, it is integrated into the RBDO formulation to obtain a reliable solution that meets the overall reliability targets while considering both model and parameter uncertainties.

The fatigue analysis of a full car body requires the sheet metal (sheet fatigue), spot welds (spot weld fatigue) and seam welds (seam weld fatigue) to be thoroughly evaluated for durability. Traditionally this has always been done in the time domain, but recently new frequency domain techniques are able to perform these tasks with numerous advantages. This paper will summarize the frequency domain process and then compare the results and performance against the more usual time domain process.

This study deals with passenger side restraint system design for frontal impact and four impact modes are considered in optimization. The objective is to minimize the Relative Risk Score (RRS), defined by the National Highway Traffic Safety Administration (NTHSA)'s New Car Assessment Program (NCAP). At the same time, the design should satisfy various injury criteria including HIC, chest deflection/acceleration, neck tension/compression, etc., which ensures the vehicle meeting or exceeding all Federal Motor Vehicle Safety Standard (FMVSS) No. 208 requirements. The design variables include airbag firing time, airbag vent size, inflator power level, retractor force level. Some of the restraint feature options (e.g., some specific features on/off) are also considered as discrete design variables. Considering the local variability of input variables such as manufacturing tolerances, the robustness and reliability of nominal designs were also taken into account in optimization process.

Generally within engineering design, the current emphasis is on biasing the development process towards increased virtual prototyping and reduced “real” prototyping. Therefore there is a requirement for more CAE based automated optimisation, Design of Experiments and Design for Six Sigma. The main requirements for these processes are that the model being analysed is parametric and that the solution time is short. Prediction of gear whine behaviour in automatic transmissions is a particularly complex problem where the conventional FEA approach precludes the rapid assessment of “what if?” scenarios due to the slow model building and solution times. This paper will present an alternative approach, which is a fully parametric functionality-based model, including the effects of and interactions between all components in the transmission. In particular the time-varying load sharing and misalignment in the planetary gears will be analysed in detail.

Automating road vehicle control can increase the range and reliability of dynamic testing. Some tests, for instance, specify precise steering inputs which human test drivers are only able to approximate, adding uncertainty to the test results. An automated steering system has been developed which is capable of removing these limitations. This system enables any production car or light truck to follow a user-defined path, using global position feedback, or to perform specific steering sequences with excellent repeatability. The system adapts itself to a given vehicle s handling characteristics, and it can be installed and uninstalled quickly without damage or permanent modification to the vehicle.

Vehicle sensitivity to brake induced vehicle vibration has been one of the key factors impacting overall vehicle quality. This directly affects long term customer satisfaction. The objective of this investigation is to understand the sensitivities of a given suspension, and steering system with respect to brake induced vehicle vibration, and develop possible solutions to this problem. Design of experiment methods have been used for this chassis system sensitivity study. The advantage of applying the design of experiment methodology is that it facilitates an understanding of the interactions between the hardware components and the sensitivity of the system due to the component change. The results of this investigation have indicated that the friction of suspension joints may affect vehicle system response significantly.

A simple and accurate analytical expression relating the expected process (long term) and sampling (short-term) product variability is developed using a variational mathematical principle. Of the several complex functional forms discovered, simplicity and ease of use are used to select an expression providing the most reliable estimation for and convenient expression of Z score (σ level) as a function of defects per opportunity (DPO) or per million opportunities (DPMO). In the absence of scientific calculators or computers, this expression allows engineers to accurately estimate long term process variability to within 0.01 of its true value without resulting to (laborious) tables or a computer. Also, a high precision approximation is provided for cases when DPO is less than 1% which estimates Z-score to within 0.003 of the actual value (at 6σ).

Global concerns involving smog, ozone formation, carcinogens and greenhouse gases have produced increasingly stringent governmental emission regulations worldwide. In the United States, the Environmental Protection Agency (EPA) introduced Tier II emissions regulations and the California Air Resources Board (CARB) established Low Emission Vehicles (LEV II) and Partial Zero Emission Vehicles (PZEV) legislation. These initiatives have created the most stringent emissions regulations to date. Vehicle manufacturers have had to improve their evaporative emission control systems to comply with these standards. The evaporative emission control system is engineered to protect our environment from fuel vapor emissions. The carbon canister is the most important component of the evaporative emissions system due to its ability to capture fuel vapors continuously during the life of the vehicle. Ford Motor Company redesigned its carbon canisters after utilizing Experimental Design techniques.

In an endeavor to improve upon historically subjective and hardware-based steering tuning development, a team was formed to find an optimal and objective solution using Design For Six Sigma (DFSS). The goal was to determine the best valve assembly design within a hydraulic power-steering assist system to yield improved steering effort and feel robustness for all vehicle models in a future truck program. The methodology utilized was not only multifaceted with several Design of Experiments (DOEs), but also took advantage of a CAE-based approach leveraging modeling capabilities in ADAMS for simulating full-vehicle, On-Center Handling behavior. The team investigated thirteen control factors to determine which minimized a realistic, compounded noise strategy while also considering the ideal steering effort function (SEF) desired by the customer. In the end, it was found that response-dependent variability dominated the physics of our valve assembly design concept.

Adopting robust design principles is a proven methodology for increasing design reliability. General Motors Powertrain (GMPT) has incorporated robust design principles into their Signal Delivery Subsystem (SDSS) development process by moving traditional prototype manufacturing and test functions from hardware to software. This virtual manufacturing technique, where subsystems are built and tested using simulation software, increases the number of possible prototype iterations while simultaneously decreasing the time required to gather statistically meaningful test results. This paper describes how virtual manufacturing was developed using distributed computing.

Robust assessment using standardized signal-to-noise (SS/N) is a Design For Six Sigma (DFSS) methodology used to assess the mating quality of USCAR electrical connectors. When the insertion force vs. distance relationship is compared to a standard under varying environmental and system-related noise conditions, the ideal function is transformed into a linear relationship between actual and ideal force at the sample points acquired during the mating displacement. Since the ideal function used in the robust assessment of competing designs has a linear slope of 1 through the origin, the SS/N function used is of the form 10 log (1/σ2), also known as nominal-the-best type 2. Using this assessment methodology, designs are compared, with a higher SS/N indicating lower variation from the standard.

Binary Logistic Regression is a powerful tool to apply in Six Sigma projects, when the response is characterized as an attribute. This paper has the purpose to present a case study based on Binary Logistic Regression application in a Six Sigma DMAIC project, where the output of the process could only be measured as “component assembled Ok or component assembled Not Ok” - a binary response.

The relation cost versus performance in the design of an automobile is crucial for its success. These two characteristics, much like the project development timing, are closely related to the attributes that the new design must achieve (e.g. weight, fuel economy, torsional stiffness, NVH, safety, etc.). In this respect, the design optimization of body reinforcements (i.e. part thickness, quantity of reinforcements, and number of spot welds) contributes greatly to a sound and robust project concept. This paper describes one application of 6-Sigma methodology to evaluate the performance of different configurations of seat belt reinforcements resulting in an optimized concept that achieved the proposed performance targets with weight and sub-assembly complexity reduction. Using a Design of Experiments (DOE) and Finite Element Analysis (FEA), each proposal was evaluated for its resistance to plastic deformation.

This paper explores the interrelation of Six Sigma and TRIZ. The use of Six Sigma DMAIC and/or DCOV principles with merging of inventive principles of TRIZ is a suggestion of paths forward to reduce the time to solve problems. The search for solutions is paralyzed in some circumstances because of psychological inertia because of it is natural for people to rely on their own experience and not think outside their comfort spot. Six Sigma pollinated with TRIZ is an opportunity to find the ideal final result. A case study on a Truck Turn Signal is used to illustrate the idea.

The stabilizer bar attachments problem can not be simply analyzed by using linear FEA methodology. The large deformation in the bushing, the elastic-plastic material property in the bushing retainer bracket, and the contact between different parts all add complexity to the problem and result in the need for an analysis method using a non-linear code, such as ABAQUS. The material properties of the bushing were experimentally determined and applied to the CAE model. It was found that using strains to estimate the fatigue life was more accurate and reliable than using stress. Many modeling techniques used in this analysis were able to improve analysis efficiency.

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.

This paper demonstrates the benefit of using simulation and robust optimization for the problem of balancing vehicle noise, vibration, and ride performance over road impacts. The psychophysics associated with perception of vehicle performance on an impact is complex because the occupants encounter both tactile and audible stimuli. Tactile impact vibration has multiple dimensions, such as impact hardness and memory shake. Audible impact sound also affects occupant perception of the vehicle quality. This paper uses multiple approaches to produce the similar, robust, optimized tuning strategies for impact performance. A Design for Six Sigma (DFSS) project was established to help identify a balanced, optimized solution. The CAE simulations were combined with software tools such as iSIGHT and internally developed Kriging software to identify response surfaces and find optimal tuning.

Six Sigma represents a mindset change – part of this mindset, is to focus our decision based on data, looking for the root causes of our issues instead of acting on the effects of the causes. Aligned to this statement, the purpose of this paper is to present through a case study, how the concepts of Six Sigma – a data driven mindset, can be used to improve a process, reducing waste and keeping the same standards of quality. The focus is to show how a transfer function, generated through a multiple regression can optimize a production process, reducing waste and improving quality.