Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,631
result(s) for
"Industrial engineering Statistical methods."
Sort by:
Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems
Rare event probability (10-4 and less) estimation has become a large area of research in the reliability engineering and system safety domains.A significant number of methods have been proposed to reduce the computation burden for the estimation of rare events from advanced sampling approaches to extreme value theory.
eBook
Industrial Methods for the Effective Development and Testing of Defense Systems
During the past decade and a half, the National Research Council, through its Committee on National Statistics, has carried out a number of studies on the application of statistical methods to improve the testing and development of defense systems.
eBook
Design of experiments for engineers and scientists
The tools and technique used in the Design of Experiments (DOE) have been proved successful in meeting the challenge of continuous improvement over the last 15 years. However, research has shown that applications of these techniques in small and medium-sized manufacturing companies are limited due to a lack of statistical knowledge required for their effective implementation. Although many books have been written in this subject, they are mainly by statisticians, for statisticians and not appropriate for engineers.Design of Experiments for Engineers and Scientists overcomes the problem of statistics by taking a unique approach using graphical tools. The same outcomes and conclusions are reached as by those using statistical methods and readers will find the concepts in this book both familiar and easy to understand. The book treats Planning, Communication, Engineering, Teamwork and Statistical Skills in separate chapters and then combines these skills through the use of many industrial case studies. Design of Experiments forms part of the suite of tools used in Six Sigma.Key features:* Provides essential DOE techniques for process improvement initiatives* Introduces simple graphical techniques as an alternative to advanced statistical methods - reducing time taken to design and develop prototypes, reducing time to reach the market* Case studies place DOE techniques in the context of different industry sectors* An excellent resource for the Six Sigma training programThis book will be useful to engineers and scientists from all disciplines tackling all kinds of manufacturing, product and process quality problems and will be an ideal resource for students of this topic.Dr Jiju Anthony is Senior Teaching Fellow at the International Manufacturing Unit at Warwick University. He is also a trainer and consultant in DOE and has worked as such for a number of companies including Motorola, Vickers, Procter and Gamble, Nokia, Bosch and a large number of SMEs.
eBook
Statistical analysis of profile monitoring
A one-of-a-kind presentation of the major achievements in statistical profile monitoring methods Statistical profile monitoring is an area of statistical quality control that is growing in significance for researchers and practitioners, specifically because of its range of applicability across various service and manufacturing settings.
eBook
An exploration of how creativity, functionality, and aesthetics are related in design
Creativity is considered to have a significant impact on the design process and its outcomes, while aesthetics and functionality are considered key characteristics of products. A relationship between creativity, aesthetics and functionality is, therefore, often assumed, however, researchers view the relations between creativity, functionality and aesthetics differently. In this paper, the authors present first evidence that novelty, usefulness and surprise are the core elements of design creativity. The aim of this research is the exploration of the relations between functionality, aesthetics, novelty, usefulness, surprise, and overall creativity, by means of an experimental case study involving design experts evaluating forty-five design samples. Statistical analysis has been conducted to investigate and understand these relations. The results obtained indicate that aesthetics has a significant positive relationship with creativity but that functionality does not have a statistically significant relationship with creativity in general. Further analysis confirms that design creativity is strongly and positively related to novelty and surprise, but not significantly related to usefulness. In addition, high correlation coefficient values have revealed that creativity, novelty and surprise are perceived as the same dimension as are functionality and usefulness. This paper may be of interest to researchers, practitioners, and educators in the broader realm of design, including industrial design, creativity in design, engineering design, design innovation, product design and new product development. It provides new insights into how creativity is perceived within the field and offers a new point of view on creativity and its dimensions for the community to meditate and to debate.
Journal Article
Guidelines for process safety metrics
The purpose of this book is to provide guidance to many levels of the organization when implementing or improving existing corporate process safety metrics. Although the process safety leaders in the company will have the strongest interest, it is equally important that others in leadership roles also read this book and work together with the process safety leaders in selecting and implementing the appropriate metric programs. This book provides guidelines and examples of effective practices for the development and use of process safety leading and lagging metrics; while also providing basic information about process safety performance indicators such as the what, when, where and why they are useful. The book explains how to calculate the three global lagging metrics recommended by CCPS. Finally, the book also provides sufficient examples such that readers gain an understanding of how performance metrics can be successfully applied over the short and long term.
STANDARD PUBLISHER DISCLAIMER
It is sincerely hoped that the information presented in this document will lead to an even more impressive safety record for the entire industry; however, the American Institute of Chemical Engineers, its consultants, CCPS Subcommittee members, their employers, their employers' officers and directors, the document's authors, and knovel and knovel Service disclaim making or giving any warranties or representations, express or implied, including with respect to fitness, intended purpose, use or merchantability and/or correctness or accuracy of the content of the information presented in this document. As between (1) American Institute of Chemical Engineers, its consultants, CCPS Subcommittee members, their employers, their employers' officers and directors, the document's authors, and knovel and knovel Service and (2) the user of this document, the user accepts any legal liability or responsibility whatsoever for the consequence of its use or misuse.
eBook
Recent advances in surface defect inspection of industrial products using deep learning techniques
Manual surface inspection methods performed by quality inspectors do not satisfy the continuously increasing quality standards of industrial manufacturing processes. Machine vision provides a solution by using an automated visual inspection (AVI) system to perform quality inspection and remove defective products. Numerous studies and works have been conducted on surface inspection algorithms. With the advent of deep learning, a number of new algorithms have been developed for better inspection. In this paper, the state-of-the-art in surface defect inspection using deep learning is presented. In particular, we focus on the inspection of industrial products in semiconductor, steel, and fabric manufacturing processes. This work makes three contributions. First, we present the prior literature reviews on vision-based surface defect inspection and analyze the recent AVI-related hardware and software. Second, we review traditional surface defect inspection algorithms including statistical methods, spectral methods, model-based methods, and learning-based methods. Third, we investigate recent advances in deep learning-based inspection algorithms and present their applications in the steel, fabric, and semiconductor industries. Furthermore, we provide information on publicly available datasets containing surface image samples to facilitate the research on deep learning-based surface inspection.
Journal Article