Advances in Additive Manufacturing and Their Applications (2nd Edition): Real-Time Modeling for Design and Control of Material Additive Manufacturing Processes

The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes.
Consequently, many multidisciplinary areas, such as advanced sensor technologies, material science, data analytics, and machine learning algorithms, are employed to create these hybrid systems.
Meanwhile, new additive manufacturing (AM) processes for metals and polymers, based on emerging technologies, have shown promise for the manufacturing of sophisticated parts with complex
geometries. These processes are undergoing a major transformation with the advent of digital
technology, hybrid physical-data-driven modeling, and fast-reduced models. This study presents a
fresh perspective on hybrid physical-data-driven and reduced order modeling (ROM) techniques
for the digitalization of AM processes within a digital twin concept. The main contribution of this
study is to demonstrate the benefits of ROM and machine learning (ML) technologies for process
data handling, optimization/control, and their integration into the real-time assessment of AM
processes. Therefore, a novel combination of efficient data-solver technology and an architecturally
designed neural network (NN) module is developed for transient manufacturing processes with high
heating/cooling rates. Furthermore, a real-world case study is presented, showcasing the use of
hybrid modeling with ROM and ML schemes for an industrial wire arc AM (WAAM) process.