Digital twin volume registration for Voxel-based closed-loop machining systems
Collins, James S.
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The goal of representing the physical state of a part throughout the manufacturing process as a digital twin has become a popular topic in recent years. With new advancements in additive-subtractive (hybrid) technologies, the need to gather and process spatial information from inside a CNC system has intensified. Open-loop g-code execution continues to operate with no feedback to describe the current state of the workpiece. It is evident that scanning sensors must be integrated into future machining systems in order to construct a closed-loop architecture whereby the controller can process geometric data to update subsequent commands. This dynamic, closed-loop g-code architecture will revolutionize manufacturing. In order to advance the research in close-loop machining systems, this thesis presents a simple but novel technique for voxel volume model registration. This is done through the application of registering and machining near-net-shape structures and rough castings. Through the implementation of a Euclidean distance transform and variance calculation, an intensity-based similarity metric is demonstrated over a discrete voxel domain driven by a metaheuristic registration algorithm. Simulation tests conducted over a uniform grid structure show that the technique is successful in positioning a floating volume inside its corresponding near-net-shape. Results for six 0.1mm resolution voxel models are reported followed by the metric’s performance under different starting conditions and registration constraints. Tests indicate that the technique works best for narrow to moderately offset volumes. The technique is presented as a prototype to demonstrate the viability of the method. Further applications and refinements of this simple technique will provide engineers with an additional method for part registration to be used in future developments of closed-loop machining systems.