Machine Shop Trade Secrets


Book Description

Written by an experienced machinist and plastic injection mold maker, this groundbreaking manual will have users thinking and producing like experienced machinists. it provides practical "how-to" information that can immediately be used to improve one's machining skills, craftsmanship, and productivity.




Fundamentals of Machine Theory and Mechanisms


Book Description

This book develops the basic content for an introductory course in Mechanism and Machine Theory. The text is clear and simple, supported by more than 350 figures. More than 60 solved exercises have been included to mark the translation of this book from Spanish into English. Topics treated include: dynamic analysis of machines; introduction to vibratory behavior; rotor and piston balanced; critical speed for shafts; gears and train gears; synthesis for planar mechanisms; and kinematic and dynamic analysis for robots. The chapters in relation to kinematics and dynamics for planar mechanisms can be studied with the help of WinMecc software, which allows the reader to study in an easy and intuitive way, but exhaustive at the same time. This computer program analyzes planar mechanisms of one-degree of freedom and whatever number of links. The program allows users to build a complex mechanism. They can modify any input data in real time changing values in a numeric way or using the computer mouse to manipulate links and vectors while mechanism is moving and showing the results. This powerful tool does not only show the results in a numeric way by means of tables and diagrams but also in a visual way with scalable vectors and curves.




Scientific Management


Book Description




The Knowledge Machine: How Irrationality Created Modern Science


Book Description

“The Knowledge Machine is the most stunningly illuminating book of the last several decades regarding the all-important scientific enterprise.” —Rebecca Newberger Goldstein, author of Plato at the Googleplex A paradigm-shifting work, The Knowledge Machine revolutionizes our understanding of the origins and structure of science. • Why is science so powerful? • Why did it take so long—two thousand years after the invention of philosophy and mathematics—for the human race to start using science to learn the secrets of the universe? In a groundbreaking work that blends science, philosophy, and history, leading philosopher of science Michael Strevens answers these challenging questions, showing how science came about only once thinkers stumbled upon the astonishing idea that scientific breakthroughs could be accomplished by breaking the rules of logical argument. Like such classic works as Karl Popper’s The Logic of Scientific Discovery and Thomas Kuhn’s The Structure of Scientific Revolutions, The Knowledge Machine grapples with the meaning and origins of science, using a plethora of vivid historical examples to demonstrate that scientists willfully ignore religion, theoretical beauty, and even philosophy to embrace a constricted code of argument whose very narrowness channels unprecedented energy into empirical observation and experimentation. Strevens calls this scientific code the iron rule of explanation, and reveals the way in which the rule, precisely because it is unreasonably close-minded, overcomes individual prejudices to lead humanity inexorably toward the secrets of nature. “With a mixture of philosophical and historical argument, and written in an engrossing style” (Alan Ryan), The Knowledge Machine provides captivating portraits of some of the greatest luminaries in science’s history, including Isaac Newton, the chief architect of modern science and its foundational theories of motion and gravitation; William Whewell, perhaps the greatest philosopher-scientist of the early nineteenth century; and Murray Gell-Mann, discoverer of the quark. Today, Strevens argues, in the face of threats from a changing climate and global pandemics, the idiosyncratic but highly effective scientific knowledge machine must be protected from politicians, commercial interests, and even scientists themselves who seek to open it up, to make it less narrow and more rational—and thus to undermine its devotedly empirical search for truth. Rich with illuminating and often delightfully quirky illustrations, The Knowledge Machine, written in a winningly accessible style that belies the import of its revisionist and groundbreaking concepts, radically reframes much of what we thought we knew about the origins of the modern world.




Machine Learning for Planetary Science


Book Description

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. - Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials - Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets - Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems - Utilizes case studies to illustrate how machine learning methods can be employed in practice




Welcome to the Machine


Book Description

Jensen and Draffan look at the way machine readable devices that track our identities and purchases have infiltrated our lives and have come to define our culture.




A Brief Illustrated History of Machines and Mechanisms


Book Description

Machines have always gone hand-in-hand with the cultural development of m- kind throughout time. A book on the history of machines is nothing more than a specific way of bringing light to human events as a whole in order to highlight some significant milestones in the progress of knowledge by a complementary persp- tive into a general historical overview. This book is the result of common efforts and interests by several scholars, teachers, and students on subjects that are connected with the theory of machines and mechanisms. In fact, in this book there is a certain teaching aim in addition to a general historical view that is more addressed to the achievements by “homo faber” than to those by “homo sapiens”, since the proposed history survey has been developed with an engineering approach. The brevity of the text added to the fact that the authors are probably not com- tent to tackle historical studies with the necessary rigor, means the content of the book is inevitably incomplete, but it nevertheless attempts to fulfil three basic aims: First, it is hoped that this book may provide a stimulus to promote interest in the study of technical history within a mechanical engineering context. Few are the co- tries where anything significant is done in this area, which means there is a general lack of knowledge of this common cultural heritage.




Machine Shop Practice


Book Description

Details the skills involved in operating milling cutters, planers, lathes, shaper tools, boring machines, grinding wheels, and drills.




Machine Learning and Data Science in the Power Generation Industry


Book Description

Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls




Machine Learning and Data Science


Book Description

MACHINE LEARNING AND DATA SCIENCE Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms. These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.