Monitoring and Control of Laser Additive Manufacturing Process: Theory and Applications (Additive Manufacturing Technology) - Tapa dura

Acerca del autor

Kunpeng Zhu is a Professor and the Head of the Precision Manufacturing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Changzhou, China. He is also the founder of Precision Manufacturing Institute, Wuhan University of Science and Technology. His research focuses on manufacturing automation, encompassing additive manufacturing, machining process modeling and monitoring, and manufacturing informatics. He is the chief scientist of national key research project and the recipient of scientific award of China. Dr. Zhu is an associate editor of Virtual and Physical Prototyping, IEEE Trans. on Automation Science and Engineering, etc. 

Ying-Hsi Jerry Fuh is a Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS) and the founding Director of the NUS Centre for Additive Manufacturing. He earned his Ph.D. from UCLA in 1992. His research has elucidated melt pool instability mechanisms, established mappings between process signals and product quality, and revealed the bone regeneration mechanisms of bio-inspired scaffolds. A Fellow of both SME and ASME, Professor Fuh has received IES Prestigious Engineering Achievement Award. He has authored over 400 research publications with more than 22,000 citations.

De la contraportada

This book aims to develop methodologies for enhancing the build quality, automation, and productivity of laser-based additive manufacturing (LAM) systems. Effective online monitoring and intelligent process control methods provide the technical foundation for realizing such advanced LAM systems. Following a comprehensive introduction to LAM processes, including process classifications, process design, material considerations, and molten pool dynamics, the book presents a general framework for the design and implementation of an intelligent LAM monitoring system.

Adopting a data-driven approach, the book elaborates on state-of-the-art techniques in measurement, signal processing, and machine learning for process monitoring and intelligent control, with the goal of improving quality, reliability, and production efficiency. Within this framework, both fundamental theories and recent advances in machine vision, deep learning, and digital twin technologies for LAM systems are introduced and discussed.

The book includes over 120 figures and diagrams to illustrate key concepts and results. It integrates the author's research and teaching experience in the field and serves as a valuable reference for researchers, graduate students, and engineers, as well as a supplementary resource for advanced undergraduate students.