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 authors 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.
