Additive manufacture (3d. printing) of plasma diagnostic components and assemblies for fusion experiments

Additive manufacturing (or 3D printing) is now becoming sufficiently accurate with a large range of materials for use in printing sensors needed universally in fusion energy research. Decreasing production cost and significantly lowering design time of energy subsystems would realize significant cost reduction for standard diagnostics commonly obtained through research grants. There is now a well-established set of plasma diagnostics (see e.g. [1]), but remain one of the most expensive subsystems in any fusion system since for every system they have to be custom built, and time for diagnostic development can sometimes pace the project. Additive manufacturing (3D printing) is developing rapidly, including open source designs. These basic components can be printed for (in some cases) less than 1/100th costs of conventional manufacturing. In our DOE Phase I SBIR we have examined the impact that AM can have on plasma diagnostic cost by taking 15 separate diagnostics through an engineering design using Conventional Manufacturing (CM) techniques to determine costs of components and labor costs associated with getting the diagnostic to work as intended. With that information in hand, we set about optimizing the design to exploit the benefits of AM. The impact on cost is found to be in several areas. First, the cost of materials falls because AM parts can be manufactured without waste, and engineered to use less material than CM. Next, the cost of fabrication falls for AM parts relative to CM since the fabrication process can lead to the combination of multiple components as highly complex monoliths (which can be manufactured less expensively than separate unit components). We find that complexity comes at no additional cost (cooling channels for example can be built in to plasma-facing components at no extra cost). Costs associated with assembly are lower for AM because many components can be combined and printed as monoliths, thereby mitigating the need for e.g. alignment, or calibration. Finally, the cost of engineering is impacted by exploiting AM design tools that allow standard components to be customized through web-interfaces. We find that concept design costs can also be impacted by scripting interfaces to the online engineering design tools. In Phase II, we will exploit advances in additive manufacturing technology for novel materials, multi- materials, and tolerances required for manufacture of most, if not all major diagnostic systems, continuing our design work, and optimizing designs of principle diagnostics for AM. As with Phase I, all designs will be made available for free to those who may wish to print up the diagnostic components themselves. One principle issue with AM is the qualification of materials for use in the fusion environment, and so we will work to meet technical specifications of major fusion facilities. Our commercialization plan is to use AM to develop diagnostics, offering complete systems at substantially lower cost than conventional manufacturing. We will prove out the technology in fusion applications before servicing a wider scientific instrumentation market worldwide.