Intermittent adaptive trajectory planning for geometric defect correction in large-scale robotic laser directed energy deposition based additive manufacturing

Laser directed energy deposition (LDED), a class of additive manufacturing (AM) processes, has immense potential to be used for various engineering applications to build components with medium to high complexity. However, dimensional deviations from intended values and inadequate surface quality of the built parts limit its wide deployment. The present work reports the development of an adaptive tool path trajectory platform to correct the dimensional inaccuracies in-situ to build high-quality components using LDED. The study deploys a laser line scanner to scan the part after the deposition of the definite number of layers followed by the detection of concave, convex, and flat surfaces using deep learning. Further, a novel adaptive trajectory planning algorithm is deployed by using three different strategies to control material deposition on concave, convex, and flat surfaces. The material deposition is controlled by using adaptive scanning speed control (ASSC), and a combination of laser on–off and scanning speed control (ASSLC). Subsequently, the built geometries are subjected to geometric, microstructure, and mechanical characterizations. It is observed that the deviation of the part was reduced by 30%, and 27.5% using ASSC and ASSLC, respectively. The structures built using the three strategies show some micropores at isolated locations. The microstructure is mainly cellular under all conditions with a similar average microhardness of ~ 210 HV. The study provides an integrated and comprehensive approach for building high-quality large-scale components using LDED with minimal dimensional deviation from the original CAD model.