Ricerc@Sapienza

The Selective Laser Melting (SLM) is a promising Additive Manufacturing technology able to fabricate full density and high-performance 3D complex objects. However, the current attainable surface roughness is a limitation for industrial employment since it often requires secondary finishing operation which gives technological constraints and additional costs. Notwithstanding the efforts paid by the researchers, the surface improvement of those materials characterized by high reflectivity and conductivity (e.g. Aluminum alloys) are yet marginal. Remelting techniques have been proposed but they are limited to flat surfaces. In this project a new idea in the SLM processing is conceived: the aim is to provide an innovative way to promote a skin remelting stage which is applied to any inclined zones in a customizable way.
The remelting process is investigated and modeled to explore the obtainable roughness improvements together with possible underskin defects. The employment of the machine learning allows to classify and predict a complex set of surface quality outcomes (e.g. roughness parameters, defects, cracks and microstructure) as a function of several processing parameters. The feasibility of the methodology is strictly linked to the 3D data management. The method implementation is divided into two main stages: a new way to define 3D surface requirements and a designed for the purpose geometry segmentation which allows the material definition; a huge material database to be used directly in the SLM machine with an automatic coding. The collection of the abovementioned tools will allow to enrich the at present SLM technology with a locally customizable laser finishing operation in the same single-step process. Many other goals will be reach: drastic reduction of attainable roughness, assigning of a desired roughness to specific zones, fabrication of homogeneous quality parts, optimization of production time vs part quality vs part defects.