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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.
2016-04-05
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
2016-01-0271
David A. Warren
The objective of the paper is to outline the steps taken to change the reliability and maintenance environment of a plant from completely reactive to proactive. The main systems addressed are maintenance function fulfillment with existing staffing; work order management, planning, and scheduling; preventive maintenance (PM) definition and frequency establishment; predictive maintenance (PdM) scheduling and method definition; and shutdown planning and execution. The work order management methods were evaluated and modified to provide planning and scheduling of work orders on a weekly basis. The computerized maintenance and management system (CMMS) was updated to automatically insert work orders into the backlog of work for completion. A failure modes and effects analysis (FMEA) was performed and the results of the FMEA led to implementation of the following PM and PdM activities: vibration analysis, thermal imaging, and temperature monitoring.
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
2016-01-0274
Sharon L. Honecker, David J. Groebel, Adamantios Mettas
In order to accurately predict product reliability, it is best to design a test in which many specimens are tested for a long duration. However, this scenario is not often practical due to economic and time constraints. This paper describes a reliability test in which a limited number of specimens are tested with little time remaining before the scheduled start of production. During the test, an unexpected failure mode that can be mitigated through a product redesign occurs. Because the scheduled start of production is near, there is not time to perform a test with redesigned specimens, so the current test proceeds as planned. We discuss several methods and the associated assumptions that must be made to account for the presence of the unexpected failure mode in the test data in order to make predictions of reliability of the redesigned product.
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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