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Viewing 1 to 30 of 3633
2016-04-19 ...
  • April 19-28, 2016 (4 Sessions) - Live Online
  • October 4-13, 2016 (4 Sessions) - Live Online
Training / Education Online Web Seminars
Tough times require searching for things that we can change and making them better. But so often problems are solved with 'band-aids' and not root cause solutions. This approach is getting too expensive and at best only helps companies tread water. To combat these issues and adopt a fresh approach, teams can use the methods and tools of Root Cause Problem Solving to first view problems as opportunities for improvement, identify root causes and implement solutions to prevent recurrence. Benefits include improved quality and customer satisfaction, reduced operation costs, and greater employee knowledge of work processes.
2016-04-13
Event
This session presents methods and automotive applications on how to assess reliability and robustness in product development. Topics include among others, system reliability target allocation, interval analysis in robust design and imprecise reliability assessment. It also addresses new developments and applications in the area of accelerated testing.
2016-04-13
Event
Model Validation and Verification invite papers that deal with the theoretical and/or applied aspects of one or more of the following representative topics: model development, model correlation/calibration, model verification, model validation, uncertainty quantification, uncertainty propagation, validation metrics, predictive capability assessment, etc.
2016-04-13
Event
2016-04-13
Event
The purpose of this session is to bring awareness among the automotive aerodynamics, thermal and hydraulic systems development community to address the need of reliability analysis and robust design to improve the overall product quality. This session also introduces CAE based optimization of aero-thermal and fluid systems to improve automotive fuel economy. This session presents papers covering both testing and simulation.
2016-04-12
Event
Methods for modeling uncertainty and decision making under uncertainty are presented in this session. Both theoretical developments and practical applications from the automotive industry are covered.
2016-04-12
Event
This session will address theoretical developments and automotive applications in RBDO and Robust Design. Topics include: computational algorithms for efficient estimation of reliability, Monte Carlo simulation, Bayesian reliability, Dempster-Shafer Evidence Theory, and Multi-Disciplinary Optimization, among others.
2016-04-12
Event
This session presents the theory, practices and technology used in development of trends in reliability and durability testing (ART/ADT) technology and accurate physical simulation for successful performance predicting. The purpose is covering a new ideas and unique approaches to simulation interaction of full field inputs, safety, and human factors, improvement the ART/ADT steps-components, implementation that leads to development dependability, reduce recalls, life cycle cost, time, etc.
2016-04-11 ...
  • April 11-13, 2016 (8:30 a.m. - 4:30 p.m.) - Detroit, Michigan
  • August 3-5, 2016 (8:30 a.m. - 4:30 p.m.) - Troy, Michigan
  • December 5-7, 2016 (8:30 a.m. - 4:30 p.m.) - Norwalk, California
Training / Education Classroom Seminars
RMS (Reliability-Maintainability-Safety-Supportability) engineering is emerging as the newest discipline in product development due to new credible, accurate, quantitative methods. Weibull Analysis is foremost among these new tools. New and advanced Weibull techniques are a significant improvement over the original Weibull approach. This workshop, originally developed by Dr. Bob Abernethy, presents special methods developed for these data problems, such as Weibayes, with actual case studies in addition to the latest techniques in SuperSMITH® Weibull for risk forecasts with renewal and optimal component replacement.
2016-04-11 ...
  • April 11-12, 2016 (8:30 a.m. - 4:30 p.m.) - Troy, Michigan
  • August 1-2, 2016 (8:30 a.m. - 4:30 p.m.) - Troy, Michigan
  • December 15-16, 2016 (8:30 a.m. - 4:30 p.m.) - Troy, Michigan
Training / Education Classroom Seminars
Design of Experiments (DOE) is a methodology that can be effective for general problem-solving, as well as for improving or optimizing product design and manufacturing processes. Specific applications of DOE include identifying proper design dimensions and tolerances, achieving robust designs, generating predictive math models that describe physical system behavior, and determining ideal manufacturing settings. This seminar utilizes hands-on activities to help you learn the criteria for running a DOE, the requirements and pre-work necessary prior to DOE execution, and how to select the appropriate designed experiment type to run.
2016-04-05
Technical Paper
2016-01-0267
Rahul Rama Swamy Yarlagadda, Efstratios Nikolaidis, Vijay Kumar Devabhaktuni
Over the last two decades inverse problems have become increasingly popular due to their widespread applications. This popularity continuously demands designers to find alternative methods, to solve the inverse problems, which are efficient and accurate. It is important to use effective techniques that are both highly accurate and computationally efficient. This paper presents a method for solving inverse problems through Artificial Neural Network (ANN) theory. This paper also presents a method to apply Grey Wolf optimizer (GWO) algorithm to solve inverse problems. GWO is a recent optimization method demonstrating great results. Both of the methods are then compared to traditional methods such as Particle Swarm Optimization (PSO) and Markov Chain Monte Carlo (MCMC). Four typical engineering design problems are used to compare the four methods' performance. The results show that the GWO outperforms other methods both in terms of efficiency and accuracy.
2016-04-05
Technical Paper
2016-01-0289
Balakrishna Chinta
Mahalanobis Distance (MD) is gaining momentum in many fields where classification, statistical pattern recognition, and forecasting are primary focus. It is a multivariate method and considers correlation relationships among parameters for computing generalized distance measure to separate groups or populations. MD is a useful statistic in multivariate analysis to test that an observed random sample is from a multivariate normal distribution. This capability alone enables engineers to determine if an observed sample is an outlier (defect) that falls outside the constructed (good) multivariate normal distribution. In Mahalanobis-Taguchi System (MTS), MD is suitably scaled and used as a measure of severity of an abnormality. It is obvious that computed MD depends on values of parameters observed on a random sample. All parameters may not equally impact MD. MD could be highly sensitive with respect to some parameters and less sensitive to some other parameters.
2016-04-05
Technical Paper
2016-01-0320
Tejas Janardan Sarang, Mandar Tendolkar, Sivakumar Balakrishnan, Gurudatta Purandare
In the automotive industry, multiple prototypes are used for vehicle development purpose. One of the challenging issues focused in R&D is the repeatability of durability tests, in order to get proper failure results for lifetime prediction. Durability test of a vehicle should have consistency throughout the testing period to provide accurate results for assessment and validation. The present work deals with more complex situations than what univariate methods can offer in terms of analysis. Hence, univariate analysis gives less accurate results in terms of checking the repeatability of tests. The current work deals with the development of a new repeatability analysis approach using multivariate analysis. The technique is developed with a non-parametric multivariate method called Mantel test which brings down all the complex parameters of the analysis to one number for checking the repeatability and take corrective measures accordingly.
2016-04-05
Technical Paper
2016-01-0434
Roshan N. Mahadule, Jaideep Singh Chavan
Door closing velocity (DCV) is one of the important design parameter which determines durability of the door. DCV varies according to the design and physical properties of the door. The physical properties of the Door assembly can affect DCV and may increase or decrease durability of the door and attached body components, this can be a concern when the overall vehicle durability performance is considered. This paper present a new tool that gives usable input data to durability engineers, which helps to bridge the gap between CAE simulations and physical tests while also reducing computation time.
2016-04-05
Technical Paper
2016-01-0480
Weiguo Zhang, John White, Mark Likich, Mac Lynch
The noise radiated from the snorkel of an air induction system (AIS) can be a major noise source to the vehicle interior noise. This noise source is typically quantified as the snorkel volume velocity which is directly related to vehicle interior noise through the vehicle noise transfer function. It is important to predict the snorkel volume velocity robustly at the early design stage for the AIS development. Design For Six Sigma (DFSS) is an engineering approach that supports the new product development process. The IDDOV (Identify-Define-Develop-Optimize-Verify) method is a DFSS approach which can be used for creating innovative, low cost and trouble free products on significant short schedules. In this paper, an IDD project which is one type of DFSS project using IDDOV method is presented on developing a robust simulation process to predict the AIS snorkel volume velocity. First, the IDDOV method is overviewed and the innovative tools in each phase of IDDOV are introduced.
2016-04-05
Technical Paper
2016-01-1292
Manish Dixit, V Sundaram, Sathish Kumar S
Noise pollution is a major concern for global automotive industries which propels engineers to evolve new methods to meet passenger comfort and regulatory requirements. The main purpose of an exhaust system in an automotive vehicle is to allow the passage of non-hazardous gases to the atmosphere and reduce the noise generated due to the engine pulsations. The objective of this paper is to propose a Design for Six Sigma (DFSS) approach followed to optimize the muffler for better acoustic performance without compromising on back pressure.Conventionally, muffler design has been an iterative process. It involves repetitive testing to arrive at an optimum design. Muffler has to be designed for better acoustics performance and reduced back pressure which complicates the design process even more.
2016-04-05
Technical Paper
2016-01-0270
Zhigang Wei, Limin Luo, Michael Start, Litang Gao
Statistical parameters, such as mean, standard deviation, in particular, failure probability are of significant interest to durability and reliability engineers. These parameters can be estimated from samples, however, these estimated parameters usually contain significant uncertainties and cannot be fully representative of the population, particularly, for test data with small sample sizes. Generally, sample size is a balanced result between durability/reliability performance and cost. There are several ways to characterize and quantify the uncertainty caused by the sample size effects, and one of the most commonly used engineering approach for failure probability is RxxCyy, in which xx and yy represent xx% reliability (R) and yy% confidence (C). RxxCyy criterion is commonly used in both test-to-failure method and the binomial test method [4-8].
2016-04-05
Technical Paper
2016-01-0283
Joydip Saha, Harry Chen, Sadek Rahman
More stringent Federal emission regulations and fuel economy requirements have driven the automotive industry toward more sophisticated vehicle thermal management systems which may include various new technologies such as active grill shutter, variable coolant flow control devices, PWM controlled fan and control strategies in order to best utilize the waste heat and minimize overall power consumption. With these new technologies and new devices, the comprehensive vehicle-thermal-system simulation tools are essential to evaluate and develop the optimal system solution for new cooling system architectures. This paper will discuss how the model-based vehicle thermal system simulation tools have been developed from analytical & empirical data, and have been used for assessment and development of new cooling system architectures.
2016-04-05
Technical Paper
2016-01-0074
Michael Jensen
Electronics now control or drive a large part of automotive system design and development, from audio system enhancements to improvements in engine and drive-train performance, and innovations in passenger safety. Industry estimates suggest that electronic systems account for more than 30% of the cost of a new automobile and represent approximately 90% of the innovations in automotive design. As electronic content increases, so does the possibility of electronic system failure and the potential for compromised vehicle safety. Even when designed properly, electronics can be the weakest link in automotive system performance due to variations in component reliability and environmental conditions. Engineers need to understand worst-case system performance as early in the design process as possible.
2016-04-05
Technical Paper
2016-01-0279
Chong Chen, Zhenfei Zhan, Jie Li, Yazhou Jiang, Helen YU
To reduce the computational time in the iterations of reliability-based design optimization, surrogate models are frequently utilized to approximate time-consuming computer aided engineering models. However, surrogate models introduces additional sources of uncertainty, such as model uncertainty. In this paper, an efficient uncertainty quantification method considering both model and parameter uncertainties is proposed. Firstly, the uncertainty of input parameters are represented in the form of Probability Density Function (PDF). Then, bias correction is then performed to improve the predictive capabilities of the surrogate models, whose uncertainty can be quantified as confidence intervals. Finally, Monte Carlo sampling is utilized to quantify the compound uncertainties. A numerical example and a real-world vehicle weight reduction design example are used to demonstrate the validity of the proposed method.
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