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The authors of this text seek to clarify mechanical fatigue and design problems by applying probability and computer analysis, and further extending the uses of probability to determine mechanical reliability and achieve optimization. The work solves examples using commercially available software. It is formatted with examples and problems for use in a one-semester graduate course.
The authors of this text seek to clarify mechanical fatigue and design problems by applying probability and computer analysis, and further extending the uses of probability to determine mechanical reliability and achieve optimization. The work solves examples using commercially available software. It is formatted with examples and problems for use in a one-semester graduate course.
Statistics and Probability for Engineering Applications provides a complete discussion of all the major topics typically covered in a college engineering statistics course. This textbook minimizes the derivations and mathematical theory, focusing instead on the information and techniques most needed and used in engineering applications. It is filled with practical techniques directly applicable on the job. Written by an experienced industry engineer and statistics professor, this book makes learning statistical methods easier for today's student. This book can be read sequentially like a normal textbook, but it is designed to be used as a handbook, pointing the reader to the topics and sections pertinent to a particular type of statistical problem. Each new concept is clearly and briefly described, whenever possible by relating it to previous topics. Then the student is given carefully chosen examples to deepen understanding of the basic ideas and how they are applied in engineering. The examples and case studies are taken from real-world engineering problems and use real data. A number of practice problems are provided for each section, with answers in the back for selected problems. This book will appeal to engineers in the entire engineering spectrum (electronics/electrical, mechanical, chemical, and civil engineering); engineering students and students taking computer science/computer engineering graduate courses; scientists needing to use applied statistical methods; and engineering technicians and technologists. * Filled with practical techniques directly applicable on the job* Contains hundreds of solved problems and case studies, using real data sets* Avoids unnecessary theory
Certainty exists only in idealized models. Viewed as the quantification of uncertainties, probabilitry and random processes play a significant role in modern engineering, particularly in areas such as structural dynamics. Unlike this book, however, few texts develop applied probability in the practical manner appropriate for engineers. Probability Models in Engineering and Science provides a comprehensive, self-contained introduction to applied probabilistic modeling. The first four chapters present basic concepts in probability and random variables, and while doing so, develop methods for static problems. The remaining chapters address dynamic problems, where time is a critical parameter in the randomness. Highlights of the presentation include numerous examples and illustrations and an engaging, human connection to the subject, achieved through short biographies of some of the key people in the field. End-of-chapter problems help solidify understanding and footnotes to the literature expand the discussions and introduce relevant journals and texts. This book builds the background today's engineers need to deal explicitly with the scatter observed in experimental data and with intricate dynamic behavior. Designed for undergraduate and graduate coursework as well as self-study, the text's coverage of theory, approximation methods, and numerical methods make it equally valuable to practitioners.
Probability Theory and Statistical Methods for Engineers brings together probability theory with the more practical applications of statistics, bridging theory and practice. It gives a series of methods or recipes which can be applied to specific problems. This book is essential reading for practicing engineers who need a sound background knowledge of probabilistic and statistical concepts and methods of analysis for their everyday work. It is also a useful guide for graduate engineering students.
Offering one of the field's most thorough treatments of material design principles, including a concise overview of fastener design, the Handbook of Mechanical Alloy Design provides an extensive overview of the effects of alloy compositional design on expected mechanical properties. This reference highlights the design elements that must be considered in risk-based metallurgical design and covers alloy design for a broad range of materials, including the increasingly important powder metal and metal matrix alloys. It discusses the design issues associated with carbon, alloy, and tool steels, microalloyed steels, and more. The Handbook of Mechanical Alloy Design is a must-have reference.
Striking a balance between the use of computer-aided engineering practices and classical life testing, this reference expounds on current theory and methods for designing reliability tests and analyzing resultant data through various examples using Microsoft® Excel, MINITAB, WinSMITH, and ReliaSoft software across multiple industries. The book disc
Updated and revised, this book presents the application of engineering design and analysis based on the approach of understanding the physical characteristics of a given problem and then modeling the important aspects of the physical system. This third edition provides coverage of new topics including contact stress analysis, singularity functions,
It is challenging at best to find a resource that provides the breadth of information necessary to develop a successful micro electro mechanical system (MEMS) design. Micro Electro Mechanical System Design is that resource. It is a comprehensive, single-source guide that explains the design process by illustrating the full range of issues involved,
This updated edition retains its introduction to applied fundamental statistics, probability, reliability, and decision theory as these pertain to problems in Civil Engineering. The new edition adds an expanded treatment of systems reliability, Bayesian methods, and spatial variabililty, along with additional example problems throughout. The book provides readers with the tools needed to determine the probability of failure, and when multiplied by the consequences of failure, illustrates how to assess the risk of civil engineering problems. Presenting methods for quantifying uncertainty that exists in engineering analysis and design, with an emphasis on fostering more accurate analysis and design, the text is ideal for students and practitioners of a range of civil engineering disciplines. Expands on the class-tested pedagogy from the first edition with more material and more examples; Broadens understanding with simulations coded both in Matlab and in R; Features new chapters on spatial variability and Bayesian methods; Emphasizes techniques for estimating the influence of uncertainty on the probability of failure