Statistics For Engine Optimization
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Statistics for Engine Optimization
In 11 specially commissioned articles, engineers and statisticians explain how they collaborate to use statistical techniques to expand the tool kit for designing engines, demonstrating especially how statistically designed experiments can make a major contribution to meeting existing and future demands in engine development. They discuss modeling techniques, response surface methods, multi-stage models, neural networks, Bayesian methods, optimization, emulating computer models, genetic algorithms, on-line optimization, and robust engineering design. Distributed in the US by ASME. Annotation copyrighted by Book News, Inc., Portland, OR
International Conference on Statistics and Analytical Methods in Automotive Engineering
Author: IMechE (Institution of Mechanical Engineers)
language: en
Publisher: John Wiley & Sons
Release Date: 2002-11-22
These IMechE conference transactions examine how major improvements have been made in product delivery processes by the effective use of both statistical and analytical methods, as well as examining the problems that can occur as a result of under utilization of information. This volume will be of great interest to managers, engineers, and statisticians at all levels, engaged in project management or the design and development of motor vehicles, their subsystems, and components. CONTENTS INCLUDE Applications of advanced modelling methods in engine development Application of adaptive online DoE techniques for engine ECU calibration Radial basis functions for engine modelling Designing for Six Sigma reliability Dimensional variation analysis for automotive hybrid aluminium body structures Reliability-based multidisciplinary design optimization of vehicle structures
Fault Detection, Supervision and Safety of Technical Processes 2003 (SAFEPROCESS 2003)
A three-volume work bringing together papers presented at 'SAFEPROCESS 2003', including four plenary papers on statistical, physical-model-based and logical-model-based approaches to fault detection and diagnosis, as well as 178 regular papers.