The University of Osaka · Engineering
Professor Danilo Ambrosio's research lab specializes in advanced friction stir welding (FSW) processes, focusing on optimizing tool design, process parameters, and material behavior to achieve high-strength, defect-free joints in dissimilar and lightweight alloys. The lab investigates thermomechanical phenomena, microstructural evolution, and interfacial bonding mechanisms—particularly in aluminum, steel, and titanium alloys—while integrating predictive modeling and machine learning for real-time defect detection. A key focus is on developing reliable, scalable, and cost-effective FSW solutions for industrial applications through innovative tool geometries and thermal modeling.
Figures are computed from collected data and may differ slightly.
The dissimilar friction stir lap welding of AA6061-T6 and mild steel using the hemispherical tool tilted towards the retreating side is investigated. Critical defects such as hook features and internal voids are avoided by limiting the plunge depth in the lower plate to a tenth of a millimeter. The low heat generation guaranteed by the hemispherical tool produces a nanoscale intermetallic compound layer alternatively composed of an Al-rich and a ternary Al-Fe-Mg phases. The complex and extremely
Abstract In this work, the friction stir weldability of Ti6Al4V T-joints has been investigated. Its aims are: (i) to study the influence of tool and welding parameters on weld quality, (ii) to assess the joints’ mechanical strength to foresee future applications, and (iii) to characterize Co-based FSW tools’ wear by following the wear during the tests. Welds’ defectivity is studied by cross-section macrographies analysis. Independently from welding parameters and tools, internal voids are avoide
Abstract Flow-related defects in friction stir welding are critical for the joints affecting their mechanical properties and functionality. One way to identify them, avoiding long and sometimes expensive destructive and nondestructive testing, is using machine learning tools with monitored physical quantities as input data. In this work, artificial neural network and decision tree models are trained, validated, and tested on a large dataset consisting of forces, torque, and temperature in the st
Process parameters and base material thermal properties strongly affect peak temperature evolution in friction stir welding (FSW). This work develops a semi-empirical model for peak temperature estimation during FSW of aluminium alloys. Several bead-on-plate runs were conducted on AA5083-H111, AA6082-T6 and AA7075-T6. Different rotational and welding speeds were tested while measuring the temperature with a thermocouple embedded in the tool. A polynomial model was proposed with the model's coeff
The use of a hemispherical tool tilted towards the retreating side for friction stir welding 6061-T6 aluminum alloy is investigated. Joints with smooth surfaces and without internal voids are obtained. Under the same welding and rotational speeds, adapting the tilt angle makes it possible to weld various thicknesses up to 3.5 mm. Plunge depth and tilt angle are demonstrated to be key geometrical parameters driving material flow when using the hemispherical tool. Microstructural features in the w
Abstract This paper describes a thermal numerical model accessible to all users for predicting temperature in friction stir welding from the power, material thermal properties, process parameters, tool, and plate dimensions. Starting with the information obtained from the machine, power or torque, the heat flux is modeled as a circular moving source with a diameter equal to that of the shoulder. The model calibrated in a specific setup (CNC machine) successfully predicted without recalibration t
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