Naol Dessalegn Dejene, PhD
I develop data-driven and physics-informed approaches to improve the quality, reliability, and sustainability of additively manufactured metallic components.
About me
I am a postdoctoral researcher in mechanical engineering specializing in metal additive manufacturing and machine learning. My research integrates experimental characterization, computational modeling, and data-driven intelligence to understand and optimize process–structure–property relationships in advanced metallic materials.
Research focus
Metal Additive Manufacturing
L-PBF, DED, WAAM, process optimization, defect control.
Machine Learning
Predictive modeling, optimization, feature importance.
Materials Characterization
SEM, EBSD, XRD, mechanical testing, microstructure.
Sustainable Manufacturing
Powder reuse, repair, remanufacturing, lifecycle thinking.
Computational Modeling
CFD, FEA, thermal simulation, digital-twin support.
Professional biography
I am a mechanical engineer and researcher working at the intersection of metal additive manufacturing, materials engineering, and machine learning. My work focuses on improving the quality, repeatability, and performance of additively manufactured metallic components through process optimization, material characterization, and predictive modeling.
My research experience covers Laser Powder Bed Fusion, Directed Energy Deposition, laser cladding, surface engineering, and process–structure–property relationships. I am particularly interested in physics-aware machine learning, defect prediction, mechanical-property prediction, and sustainable metal AM strategies.
Education & academic links
Postdoctoral Researcher
Research in intelligent additive manufacturing, DED cladding, and machine-learning-assisted process optimization.
PhD · Mechanical & Structural Engineering
PhD research on L-PBF-based metal AM quality prediction and process–structure–property relationships using machine learning.
MSc · Manufacturing Engineering
BSc · Mechanical Engineering
Research vision
My research aims to create reliable, interpretable, and industry-relevant approaches for metal additive manufacturing by combining experiments, numerical modeling, sensor-driven monitoring, and machine learning. The long-term goal is to support robust process windows, defect-aware manufacturing, sustainable repair, and qualification-ready metallic AM components.
L-PBF quality control
Porosity, hardness, surface roughness, scanning strategy, hatch spacing, part orientation, and repeatability.
DED & laser cladding
Surface strengthening, repair, coating integrity, dilution control, wear resistance, corrosion resistance, and interface quality.
AI for manufacturing
Machine learning, physics-aware modeling, feature importance, process correction, and multimodal monitoring.
Selected publications
Publications are numbered in reverse chronological order. The most recent appears as #18, the earliest as #1. Use the search box and year filters to narrow the list.
L-DED Surface-hardening cladding for tribo-corrosion environments
Completed · 2025Completed project on DED/laser cladding-based surface strengthening for marine applications exposed to combined wear and corrosion conditions.
DED based-Intelligent powder recycling
Ongoing · 2026Ongoing work on powder reuse, powder quality monitoring, and intelligent decision support for sustainable metal additive manufacturing.
Ongoing Alloy development with novel materials
2025 / 2026Research direction on novel alloy systems and oxide-dispersion-strengthened materials for advanced AM and high-performance applications.
Events & academic activities
06–09
2026
Teaching profile
I have experience in engineering education with a strong interest in connecting fundamental theory, practical applications, and research-based learning. My teaching interests include additive manufacturing, manufacturing processes, materials engineering, engineering mechanics, CAD/CAM, welding technology, and machine learning for engineers.
Selected courses taught
Professional experience
Postdoctoral Researcher
- Research on advanced DED cladding and intelligent additive manufacturing.
- Mentoring MSc students and co-supervising PhD student on AM-related research activities.
Assistant Professor
- Delivered MSc courses in additive manufacturing and welding.
- Supervised MSc research projects in manufacturing and welding.
PhD Research Fellow
- Developed L-PBF process-quality models and ML-based prediction workflows.
- Conducted mechanical testing, surface analysis, and AM process optimization.
Lecturer of Mechanical Engineering
- Taught undergraduate courses in manufacturing, materials, engineering mechanics, CAD/CAM, and mechanical vibration.
- Served in academic coordination and laboratory leadership roles.
Maintenance Technician
- Supported industrial maintenance activities for production and utility systems in a high-throughput brewery environment.
Contact & collaboration
I welcome collaboration opportunities in metal additive manufacturing, machine-learning-assisted process optimization, surface engineering, DED repair, L-PBF quality improvement, and sustainable manufacturing.