Danilo Ambrosio

Laboratoire Génie de Production

Papers

1

Total Citations

2

H-Index

1

About

Danilo Ambrosio’s research centers on friction stir welding (FSW) and thermal modeling, with a focus on making advanced manufacturing processes more accessible to engineers and researchers. His most-cited work introduces a power-based thermal numerical model that predicts temperature distributions in FSW using readily available inputs such as machine power, material properties, and tool geometry. This model stands out for its user-friendly design, enabling practitioners without specialized computational resources to optimize welding parameters and improve joint quality. With 2 citations to date, the paper represents a foundational step in democratizing process simulation. Ambrosio’s contributions are particularly valuable for bridging the gap between complex thermal physics and practical industrial application, offering a tool that can reduce trial-and-error in manufacturing. His work underscores a commitment to developing efficient, transferable models that enhance understanding of thermomechanical phenomena in solid-state joining. For students and researchers exploring FSW or process modeling, Ambrosio’s approach provides a clear, actionable framework for integrating machine data into predictive analysis.

Research Focus

Key Achievements

1
H-Index
1
Papers
2
Total Citations
2
Avg Citations/Paper
🏆 Most Cited Paper
Power-based Model for Temperature Prediction in FSW
2 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 6
🏛 Institutions: Laboratoire Génie de Production

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 11 days ago