SUMMARY

Metal additive manufacturing offers component designers the freedom to explore complex part geometries otherwise infeasible with legacy manufacturing methods; however, the cost-per-part associated with additive manufacturing hinders its adoption, despite its benefits. This work explores parallelization in hybrid additive-subtractive manufacturing, addressing research gaps in the areas of process simulation, task scheduling, and optimal motion planning. Where hybrid manufacturing is usually performed iteratively with additive and subtractive operations not overlapping, parallelizing hybrid manufacturing through concurrent additive and hybrid operations can reduce production time. The present work addresses challenges in a concurrent hybrid manufacturing system, comprised of a robotic arm for additive manufacturing and a CNC mill for subtractive manufacturing. Currently, simulation software for subtractive manufacturing exists where custom preform geometry can be used as the stock material. These simulations can take seconds to minutes to run, allowing for rapid iterations. Similar rapid simulations for additive manufacturing are less mature; however. Here, an implicit model for wire arc additive manufacturing is explored as a method for rapidly simulating depositions while being agnostic to print parameters. During concurrent hybrid operation, several challenges exist in the control and path planning of a robot-CNC system. Task allocation for multi-robot additive systems has been explored, but in these studies, the robots are not kinematically linked, as they are in the robot-CNC system. New heuristic methods are introduced here for finding concurrent hybrid task schedules that minimize make-span while also respecting the kinematics of the system. Lastly, motion planning for the robot-CNC system is investigated. Trajectory and motion planning for robot and CNC systems individually have been extensively explored. This is not the case for robot-CNC combinations, however. A robot-CNC combination brings the additional challenge of CNC motion affecting robot motion. Optimal trajectories are generated that take the requirements of both processes into account as well as the link between the robot and CNC kinematics. With the proposed advances, concurrent hybrid manufacturing can not only be realized but in a way that maximizes process parallelization, potentially allowing for wider adoption of additive manufacturing.