A novel self-repairing additive manufacturing system for in-situ defects detection and correction

The layerwise nature of laser powder bed fusion processes (LPBF) allows the inline acquisition of a wide spectrum of information about the process stability and the part quality. Many studies discussed the possibility of using high-resolution imaging of each layer to detect defects related to parts geometry and dimension and powder bed deposition flaws. However, there is still a lack of instruments and methodologies to implement corrective actions during the process to get rid of defects. The paper presents the first LPBF system prototype concept that combines in-situ defect detection and defect removal capabilities thanks to a novel self-repaining solution. In-line removal of defects is achieved by single or multiple layer delation via an in-situ surface grinding operation activated by the automated defect detection algorithm. Preliminary results show that it is possible to combine these additive and subtractive processes without introducing discontinuities or external contaminations into the part. The layer removal operation was combined with a layerwise methods for geometric error detection based on an off-axis high-resolution camera. Indeed, geometrical distortions represent critical defects that can be difficult or, in many cases, impossible to correct in post-process stages. This work represents a preliminary study towards novel zero-defect and first-time-right additive production capabilities that integrate embedded intelligence with novel hybrid system configurations.