Search Results

Despite numerous research efforts, there is no reliable and widely accepted tool for the prediction of erosion prone material surfaces due to collapse of cavitation bubbles. In the present paper an Erosion Aggressiveness Index (EAI) is proposed, based on the pressure loads which develop on the material surface and the material yield stress. EAI depends on parameters of the liquid quality and includes the fourth power of the maximum bubble radius and the bubble size number density distribution. Both the newly proposed EAI and the Cavitation Aggressiveness Index (CAI), which has been previously proposed by the authors based on the total derivative of pressure at locations of bubble collapse (DP/Dt>0, Dα/Dt<0), are computed for a cavitating flow orifice, for which experimental and numerical results on material erosion have been published. The predicted surface area prone to cavitation damage, as shown by the CAI and EAI indexes, is correlated with the experiments.

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.

Students, as members of Team Paradigm, at the University of Wisconsin-Madison have designed a charge regulating, parallel hybrid electric Dodge Intrepid for the 1997 FutureCar Challenge (FCC97). The goals for the Wisconsin “FutureCow” are to achieve an equivalent fuel consumption of 26 km/L (62 mpg) and Tier 2 Federal Emissions levels while maintaining the full passenger/cargo room, appearance, and feel of a stock Intrepid. These goals are realized through drivetrain simulations, a refined vehicle control strategy, decreased engine emissions, and aggressive weight reduction. The vehicle development has been coupled with 8,000 km of reliability and performance testing to ensure Wisconsin will be a strong competitor at the FCC97.

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.

The demand for highly flexible manipulation of plant growth generations, modification of specific plant processes, and genetically engineered crop varieties in a controlled environment has led to the development of a Commercial Plant Biotechnology Facility (CPBF). The CPBF is a quad-middeck locker playload to be mounted in the EXPRESS Rack that will be installed in the International Space Station (ISS). The CPBF integrates proven ASTROCULTURE” technologies, state-of-the-art control software, and fault tolerance and recovery technologies together to increase overall system efficiency, reliability, robustness, flexibility, and user friendliness. The CPBF provides a large plant growing volume for the support of commercial plant biotechnology studies and/or applications for long time plant research in a reduced gravity environment.

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.

Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)/coir fiber composites were prepared via both conventional and microcellular injection-molding processes. The surface of the hydrophilic coir fiber was modified by alkali and silane-treatment to improve its adhesion with PHBV. The morphology, thermal, and mechanical properties were investigated. The addition of coir fiber (treated and untreated) reduced cell size and increased cell density. Further decrease in cell size and increase in cell density were observed for treated fibers compared with PHBV/untreated fiber composites. Mechanical properties such as specific toughness and strain-at-break improved for both solid and microcellular specimens with the addition of coir fibers (both treated and untreated); however, the specific modulus remained essentially the same statistically while the specific strength decreased slightly.

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.