Part I: Introduction and Fundamental -- Introduction -- Big Data Analytics and Deep Learning Algorithms -- Part II: Survey -- Intelligent Manufacturing Prognosis: A Survey -- Sustainable Manufacturing Enabled by Artificial Intelligence: A Survey -- Human-Robot Collaboration and Artificial Intelligence: A Survey -- Part III: Applications and Case Studies -- Fog Computing and Convolutional Neural Network Enabled Machining Prognosis and Optimisation -- Big Data Enabled Intelligent Immune System for Energy Efficient Manufacturing Management -- Tool Wear Prognosis Using Deep Learning Algorithms -- Big Data Analytics Supported Close-loop Machining Control and Optimisation -- Intelligent Learning from Demonstrators for Human-Robot Collaboration -- Human-Robot Collaboration and Intelligent Welding Applications -- Deep Learning Driven Intelligent Welding Robotics.

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.