Antoine Queguineur

Antoine Queguineur is a Doctoral Researcher at the Department of Automation Technology and Mechanical Engineering, Faculty of Engineering and Natural Sciences, Tampere University. His research focuses on additive manufacturing technologies, particularly directed energy deposition (DED), combining materials science with machine learning and industrial IoT. He explores multi-material design, process optimization, and real-time monitoring in manufacturing systems.

Key research areas include:

• Additive manufacturing of complex components like railway bogies and naval parts
• Material characterization of stainless steel and composites (e.g., WC-Co)
• Machine learning applications for process control (CNN-based melt pool analysis, neural networks for bead geometry modeling)
• IoT integration for data-driven manufacturing processes

Recent work emphasizes multi-objective optimization in generative design and the influence of process parameters on material properties. His studies bridge theoretical materials science with industrial-scale applications, aiming to enhance efficiency and reliability in large-component additive manufacturing.

Publications highlight advancements in DED process monitoring, material microstructure analysis, and infill strategy optimization. He has contributed to evaluating novel techniques like tandem GMAW for naval components and exploring thin/thick wall manufacturing in duplex stainless steel.