In situ sensing for zero defect additive manufacturing: a study on spatters in SLM

The market starts to be increasingly interested in Additive Manufacturing (AM) for metal parts, thanks to the possibility of overcoming different limitations of existing technologies in many fields, including the aerospace, bio-medical and tooling and moulding sectors. In order to meet the challenging constraints imposed by the industry, the quality, capability and repeatability of the AM processes represent fundamental issues. Due to the long duration of AM processes, and the difficult/expensive inspectability of complex shapes and internal structures, in-process monitoring of AM process represents a key enabling technology towards a zero-defect oriented production. Recently, the literature has been devoted to the analysis of printed part qualities and the process monitoring mainly focusing on the melt pool properties, slice-related patterns, thermal transitory and other quantities. However, there are other sources of information that have not been considered so far, which may be used to design future in-process monitoring tools. Among them, the spatters generated by the material/laser interaction are expected to enclose relevant information about the melting conditions. This study is aimed at investigating the suitability of spatters as a potential suitable information source for monitoring purposes in Selective Laser Melting (SLM). An experimental study is presented, where specimens of maraging steel were produced via SLM under different conditions, corresponding to different energy density states. The spatters behaviour was observed through a high-speed camera, and different statistical descriptors were computed via image processing and segmentation analysis. The results demonstrate that different melting conditions and different scan phases produce different quantities of spatters and different spatial dispersions of the spatters with different average sizes. Moreover, the spatter descriptors allow a better classification of melting conditions than monitoring only the light emitted by the laser-heated zone. The results of this thesis can be used, in future development, to design spatter-based process monitoring tools for SLM applications.