Condition modelling and control is a technique used to enable decision-making in manufacturing processes of interest to researchers and practising engineering. Condition Monitoring and Control for Intelligent Manufacturing will be bought by researchers and graduate students in manufacturing and control and engineering, as well as practising engineers in industries such as automotive and packaging manufacturing.

Advances in materials science and engineering have paved the way for the development of new and more capable sensors. Drawing upon case studies from manufacturing and structural monitoring and involving chemical and long wave-length infrared sensors, this book suggests an approach that frames the relevant technical issues in such a way as to expedite the consideration of new and novel sensor materials. It enables a multidisciplinary approach for identifying opportunities and making realistic assessments of technical risk and could be used to guide relevant research and development in sensor technologies.

Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as: • fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.

Precision Manufacturing provides an introduction to precision engineering for manufacturing. With an emphasis on design and performance of precision machinery for manufacturing – machine tool elements and structure, sources of error, precision machining processes and process models sensors for process monitoring and control, metrology, actuators, and machine design. This book will be of interest to design engineers, quality engineers and manufacturing engineers, academics and those who may or may not have previous experience with precision manufacturing, but want to learn more.

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0.

Manufacturing, reduced to its simplest form, involves the sequencing of product forms through a number of different processes. Each individual step, known as an unit manufacturing process, can be viewed as the fundamental building block of a nation's manufacturing capability. A committee of the National Research Council has prepared a report to help define national priorities for research in unit processes. It contains an organizing framework for unit process families, criteria for determining the criticality of a process or manufacturing technology, examples of research opportunities, and a prioritized list of enabling technologies that can lead to the manufacture of products of superior quality at competitive costs. The study was performed under the sponsorship of the National Science Foundation and the Defense Department's Manufacturing Technology Program.

Advanced Manufacturing and Automation V contains the proceedings of the 5th International Workshop of Advanced Manufacturing and Automation (IWAMA 2015). This meeting continues the success of this important international workshop series and disseminates the works of academic and industrial experts, from around the world, in the areas of advanced manufacturing and automation. The disciplines of manufacturing and automation have attained paramount importance and are vital factors for the maintenance and improvement of the economy of a nation and the quality of life. Manufacturing and automation are advancing at a rapid pace and new technologies are constantly emerging in the fields. The challenges faced by today’s engineers are forcing them to keep on top of the emerging trends through continuous research and development. The papers comprising these proceedings cover various topics including: Robotics and automation; Computational intelligence; Design and optimization; Product life-cycle management; Integration of CAD/CAPP/CAM/CIMS; Advanced manufacturing systems; Manufacturing operations management; Knowledge-based manufacturing; Manufacturing quality control and management; Sustainable production; Diagnosis and prognosis of machines; Lean and agile manufacturing; Virtual and grid manufacturing; Resource and asset management; Logistics and supply chain management; RFID applications; Predictive maintenance; Reliability and maintainability in manufacturing; Project management; Renewable energy development; Environment protection; Intelligent detection.

This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.

This thesis introduces a successfully designed and commissioned intelligent health monitoring system, specifically for use on any industrial robot, which is able to predict the onset of faults in the joints of the geared transmissions. However the developed embedded wireless condition monitoring system leads itself very well for applications on any power transmission equipment in which the loads and speeds are not constant, and access is restricted. As such this provides significant scope for future development. Three significant achievements are presented in this thesis. First, the development of a condition monitoring algorithm based on vibration analysis of an industrial robot for fault detection and diagnosis. The combined use of a statistical control chart with time-domain signal analysis for detecting a fault via an arm-mounted wireless processor system represents the first stage of fault detection. Second, the design and development of a sophisticated embedded microprocessor base station for online implementation of the intelligent condition monitoring algorithm, and third, the implementation of a discrete wavelet transform, using an artificial neural network, with statistical feature extraction for robot fault diagnosis in which the vibration signals are first decomposed into eight levels of wavelet coefficients.

To engineer and manufacture is human. Manufactured goods are subjected to severe international competitive forces. Consumers' perceptions towards total quality, reliable performance, health and safety, environmental issues, energy conservation and cost of ownership are changing day by day. Manufacturers have no alternative but to satisfy the consumer's increasing demands with maximum efficiency and profitability with minimum delay. Failure to meet such a challenge is clearly undesirable and will, no doubt, result in the closure of manufacturing activities, which is still regarded by many as the backbone of our national economy. Manufacturing for profitability should be the number one concern of all serious minded and responsible people. To help the industries to meet these challenges and to manage efficiently well into 1990s and beyond, the Technical Advisory Committee in their wisdom decided the appropriate theme, Profitable Condition Monitoring, for this year's International Conference, to coincide with the great European market to be opened in 1993. The benefits from condition monitoring are well documented. Condition monitoring is now an affordable technology which is waiting to be fully exploited by all sectors of industry, both big and small. Many companies have realised the following benefits from condition monitoring: • optimisation of profits • maximisation of production • cost-effective maintenance • minimisation of product liability • maximisation of total quality. As the contents of this proceedings reveal, there have been a number of significant advances in condition monitoring of which companies ought to be taking full advantage.