Smart Machining Systems
Description
This book provides the tools to enhance the precision, automation and intelligence of modern CNC machining systems. Based on a detailed description of the technical foundations of the machining monitoring system, it develops the general idea of design and implementation of smart machining monitoring systems, focusing on the tool condition monitoring system.
The book is structured in two parts. Part I discusses the fundamentals of machining systems, including modeling of machining processes, mathematical basics of condition monitoring and the framework of TCM from a machine learning perspective. Part II is then focused on the applications of these theories. It explains sensory signal processing and feature extraction, as well as the cyber-physical system of the smart machining system.
Its utilisation of numerous illustrations and diagrams explain the ideas presented in a clear way, making this book a valuable reference for researchers, graduate students and engineers alike.
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Person
Professor Kungpeng Zhu received his PhD from the National University of Singapore in 2007. He then worked as a postdoctoral research fellow at the same institution, until 2011, when he moved as an Alexander von Humboldt Fellow to the Institute of Automation and Information Systems at the Technical University of Munich. In 2013, he became a professor at the Institute of Advanced Manufacturing Technology of the Chinese Academy of Sciences, where he is currently employed. He is the associate editor of the IEEE/ASME Trans Mechatronics, IEEE Trans. Automation Science and Engineering, and ISA Transactions. His research interests include precision manufacturing, additive manufacturing and cyber-physical production systems.
Content
Part I: Fundamentals.- Chapter 1. Introduction.- Chapter 2. Modeling of Machining Process.- Chapter 3. Tool Wear Modelling.- Chapter 4. Mathematical Fundamentals of Condition Monitoring.- Chapter 5. Signal Processing for Condition Monitoring.- Chapter 6. The Framework of TCM from Machine Learning View.- Part II: Applications.- Chapter 7. Sensory Signal De-noising and Pre-processing.- Chapter 8. TCM with Sparse Decomposition.- Chapter 9. The Monitoring of Tool Conditions with Computer Vision.- Chapter 10. Diagnosis and Prognosis of Machining Degradation Process.- Chapter 11. Sensor Fusion Approached to TCM.- Chapter 12. Big Data Orientated CNC Machining TXM Monitoring System.- Chapter 13. CPPS Framework of Smart CNC Machining Monitoring System.
