In recent years, interest in developing statistical and computational techniques for applied manufacturing engineering has been increased. Today, due to the great complexity of manufacturing engineering and the high number of parameters used, conventional approaches are no longer sufficient. Therefore, in manufacturing, statistical and computational techniques have achieved several applications, namely, modelling and simulation manufacturing processes, optimization manufacturing parameters, monitoring and control, computer-aided process planning, etc. The present book aims to provide recent information on statistical and computational techniques applied in manufacturing engineering. The content is suitable for final undergraduate engineering courses or as a subject on manufacturing at the postgraduate level. This book serves as a useful reference for academics, statistical and computational science researchers, mechanical, manufacturing and industrial engineers, and professionals in industries related to manufacturing engineering.

"This book contains the latest research developments in manufacturing technology and its optimization, and demonstrates the fundamentals of new computational approaches and the range of their potential application"--Provided by publisher.

This book presents computational and statistical methods used by intelligent systems within the concept of Industry 4.0. The methods include among others evolution-based and swarm intelligence-based methods. Each method is explained in its fundamental aspects, while some notable bibliography is provided for further reading. This book describes each methods' principles and compares them. It is intended for researchers who are new in computational and statistical methods but also to experienced users.

Reducing the variation in process outputs is a key part of process improvement. For mass produced components and assemblies, reducing variation can simultaneously reduce overall cost, improve function and increase customer satisfaction with the product. The authors have structured this book around an algorithm for reducing process variation that they call "Statistical Engineering." The algorithm is designed to solve chronic problems on existing high to medium volume manufacturing and assembly processes. The fundamental basis for the algorithm is the belief that we will discover cost effective changes to the process that will reduce variation if we increase our knowledge of how and why a process behaves as it does. A key way to increase process knowledge is to learn empirically, that is, to learn by observation and experimentation. The authors discuss in detail a framework for planning and analyzing empirical investigations, known by its acronym QPDAC (Question, Plan, Data, Analysis, Conclusion). They classify all effective ways to reduce variation into seven approaches. A unique aspect of the algorithm forces early consideration of the feasibility of each of the approaches. Also includes case studies, chapter exercises, chapter supplements, and six appendices. PRAISE FOR Statistical Engineering "I found this book uniquely refreshing. Don't let the title fool you. The methods described in this book are statistically sound but require very little statistics. If you have ever wanted to solve a problem with statistical certainty (without being a statistician) then this book is for you. - A reader in Dayton, OH "This is the most comprehensive treatment of variation reduction methods and insights I’ve ever seen."- Gary M. Hazard Tellabs "Throughout the text emphasis has been placed on teamwork, fixing the obvious before jumping to advanced studies, and cost of implementation. All this makes the manuscript !attractive for real-life application of complex techniques." - Guru Chadhabr Comcast IP Services COMMENTS FROM OTHER CUSTOMERS Average Customer Rating (5 of 5 based on 1 review) "This is NOT a typical book on statistical tools. It is a strategy book on how to search for cost-effective changes to reduce variation using empirical means (i.e. observation and experiment). The uniqueness of this book: Summarizes the seven ways to reduce variation so we know the goal of the data gathering and analysis, present analysis results using graphs instead of P-value, and integrates Taguchi, Shainin methods, and classical statistical approach. It is a must read for those who are in the business of reducing variation using data, in particular for the Six Sigma Black Belts and Master Black Belts. Don't forget to read the solutions to exercises and supplementary materials to each chapter on the enclosed CD-ROM." - A. Wong, Canada

Statistics is a key characteristic that assists a wide variety of professions including business, government, and factual sciences. Companies need data calculation to make informed decisions that help maintain their relevance. Design of experiments (DOE) is a set of active techniques that provides a more efficient approach for industries to test their processes and form effective conclusions. Experimental design can be implemented into multiple professions, and it is a necessity to promote applicable research on this up-and-coming method. Design of Experiments for Chemical, Pharmaceutical, Food, and Industrial Applications is a pivotal reference source that seeks to increase the use of design of experiments to optimize and improve analytical methods and productive processes in order to use less resources and time. While highlighting topics such as multivariate methods, factorial experiments, and pharmaceutical research, this publication is ideally designed for industrial designers, research scientists, chemical engineers, managers, academicians, and students seeking current research on advanced and multivariate statistics.

This completely updated volume covers tool materials, tolerances, an inspection of drilling tools, requirements of tool drawings with examples, and methodologies and procedures of failure analysis. It introduces a new line of HP drilling tools called VPA designs and brings it into sharp focus signifying its importance in drilling operations. High-Productivity Drilling Tools: Materials, Metrology, and Failure Analysis further develops the concept of the metrology of the drilling tools introduced in the first edition. For the first time, the relevant metrological parameters are clearly defined with tolerance for HPHD with practical examples of step-by-step inspection/measurement using advanced tool measurement microscopes and CNC machines. A pros and cons list as a quick and easy decision-making tool for the choice of measuring equipment for a particular application is offered along with practical examples of drilling tool drawings to help tool designers, cutting tool and manufacturing engineers, and users in their everyday activities in the design and selection of HPDT for a particular application. An unparalleled presentation of metalworking fluids (MWFs, a.k.a. coolants) is given and covers all the start of the business: selection, implementation in HPDT and drilling operation, monitoring, and maintenance. A two-step procedure for successful implementation of near-dry machining (NDM) or minimum quantity lubrication (MQL) is presented, and a discussion of the wear of the drilling tool, its proper assessments, and metrics are provided in the evaluation of tool life and quality of machined holes. This practical book should be on the shelves of all industrial engineers, those working in production and manufacturing, process designers, tool material designers, cutting tool designers, and quality specialists. Researchers, senior undergraduate students, and graduate students will also find this book full of very helpful reference information and the source of new ideas and notions in drilling tool development. This book is also available as a set - Drills: High-Productivity Drilling Tools, 2-Volume Set (9781032203508).

This book covers design of experiments (DoE) applied in production engineering as a combination of manufacturing technology with applied management science. It presents recent research advances and applications of design experiments in production engineering and the chapters cover metal cutting tools, soft computing for modelling and optmization of machining, waterjet machining of high performance ceramics, among others.