Reza Asadi

Reza Asadi is a Researcher in the Doctoral Programme in Engineering Sciences, focusing on automation technology and mechanical engineering. His research spans advanced manufacturing techniques, particularly directed energy deposition (DED) and additive manufacturing.

Research Interests include directed energy deposition, neural networks, aluminum alloy processing, and thin-walled structures. He employs machine learning (e.g., CNNs) and signal processing techniques (e.g., wavelet transforms) to optimize manufacturing processes like laser-wire DED and milling.

Recent Publications highlight deep learning for melt pool analysis, IoT systems in DED, and predictive modeling of bead geometries. Collaborations involve institutions in Finland and Europe, with key contributions to DED process monitoring and material sensitivity studies.