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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.

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.

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.

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.

Wavelets are a powerful mathematical tool used to multi-resolution time-frequency decomposition of signals, in order to analyze them in different scales and obtain different aspects of the information. Despite being a relatively new tool, wavelets have being applied in several areas of human knowledge, especially in signal processing, with emphasis in encoding and compression of image, video and audio. Based on a previous successful applications (FRAZIER, 1999) together a commitment to quality results, this paper evaluates the use of the Discrete Wavelet Transform (DWT) as an compression algorithm to reduce the amount of data collected in road load signals (load history) which are used by the durability engineering teams in the automotive industry.

Due to the challenges in quantifying hand loads in manufacturing environments it is often assumed that the load is evenly distributed between the hands, even when handling parts with non-uniform mass distribution. This study estimated hand loads for six female subjects, when handling a custom part in 8 different configurations (2 weights, 4 CofM locations). The calculated hand loads varied from 20 to 50% of the weight being handled. The magnitude of asymmetrical hand loading depended on both the part orientation and the location of the CoM. Based on this study the knowledge of part weight, CofM location and hand positioning will allow the users of digital human models to perform more realistic and reliable task analysis assessments as the force distributions will be more representative of the actual loading rather than simply assuming the load is evenly distributed between the hands.

In this study, the Gleeble test was used to investigate the effect of cooling time, which is an indication of welding heat input, on fracture toughness at the simulated HAZ of different test materials, including one mild steel and three DP600 steels from three different suppliers. One of the important findings is that the three DP600 steels have similar tensile properties and similar base metal microstructures. After different simulated welding thermal cycles, however, the microstructure, the microhardness and thus the fracture toughness of the simulated HAZ of the steels showed significant variations among the steels tested, which indicates that DP600 steels from different suppliers can have different responses to the welding heat input.

Programmable vehicle models (PVMs) are intended to save time and costs in building bucks to evaluate vehicle interior packages. This paper presents results from a series of three studies conducted to determine the applicability and limitations of the methodology of using a PVM. The PVM used for the study was built by Prefix Corporation and has forty-two axes of adjustments. The first study verified the PVM's ability to reproduce dimensions. It showed that the PVM was acceptably accurate, with most dimension measurement ranges less than 2-3 mm. The second study was conducted to determine if subject responses are reliable indicators of package differences. Aside from some special causes, most were found to be. The third study was conducted to determine how well the PVM replicated 3 actual vehicles. Despite some dimensional discrepancies, there was evidence of correlation between subject responses from the PVM and actual vehicles.

MIG weld failures are commonly seen in chassis and frame structures in automobile industry. Until now, testing and CAE analysis based on local stresses in the vicinity of MIG weld were driving the design of these welds. With the advent of advanced methods and tools, it is possible to estimate fatigue life of MIG welds and support the design in the early stages of the vehicle program. Recently, fatigue damage models are developed for assessing the durability of MIG welds in aluminum auto structures. These damage models are based on advanced technologies like mesh-insensitive structural stress method, virtual node method, estimation of notch stress intensities and life predictions based on two-stage crack growth law. This paper outlines the theoretical aspects involved in deriving the master S-N curve.

Six-Sigma provides the opportunity and discipline to eliminate mistakes, improve morale, and save money. Doing things right and keeping them consistent is the idea behind Six-Sigma. A fundamental objective of Six-Sigma is to achieve customer satisfaction with continuous improvement in quality. Process control and manufacturing variation reduction is important but companies often find that the majority of their quality problems were actually created during the design process. An example of improving manufacturing process capability to give bottom line cost savings and customer satisfaction is presented in this paper. The methodology to increase system robustness through Design for Six Sigma (DFSS) is presented and demonstrated through the extension of the case study of crankshaft journal lobing design robustness improvements.

One thing that is very important in a carmaker company is its know-how built during all its life. Such an experience allows, for instance, to correlate the customer expected product life with accelerated tests procedures. When it comes to cars, it is usual to have correlated proving routes in such way that if a prototype can take a number of passing in the proving ground without failure, it is unlikely the car is going to fail during a regular life. In the other hand, if a failure at determined percentage of the test happens, it is predictable that the same failure shows up at the same percentage of the product design life. This paper proposes a methodology based on the SxN fatigue theory to solve durability issues observed in correlated durability tests.