Autonomous and self-adapting, high-resolution 3D additive manufacturing by high energy impacts of fine particles

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The usability and efficiency of cold-spray based additive manufacturing processes depend crucially on the achieved velocities and temperatures of the particles on impact, as these are the main factors for a successful deposition on the forming layer of the component. Therefore, they define the desired window of process conditions for the overall control system. Here, two major challenges exist. The first challenge is that the achieved particle velocities and temperatures on impact strongly depend on the possibly varying particulate raw material and other unforeseen variations/disturbances in the process chain. The second challenge is that the aforementioned desired window of process conditions varies with the raw material. To solve the first problem model-based control concepts will be investigated. The second challenge will be met by developing a real-time optimization-based high-level controller, which allows for an adaptation of the desired window of process conditions during regular process operation.The envisaged hierarchical control scheme aims for autonomous cold spray based additive manufacturing robust with respect to variations in the given particulate material (chemical composition, thermal conductivity, size and shape distribution). Experimental validation of the concepts using high-speed particle image velocimetry, temperature field measurements and analysis of the produced new material layers will be performed.