Bachelor Thesis 3d Printing

Bachelor Thesis 3d Printing-62
The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.GRCop-42 is a high conductivity, high-strength dispersion strengthened copper-alloy for use in high heat flux applications such as liquid rocket engine combustion devices.

The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.GRCop-42 is a high conductivity, high-strength dispersion strengthened copper-alloy for use in high heat flux applications such as liquid rocket engine combustion devices.

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This alloy is part of the family of NASA-developed GRCop, copper-chrome-niobium alloys.

GRCop alloys were developed for harsh environments specific to regeneratively-cooled combustion chambers and nozzles with good oxidation resistance.

In this thesis, locator design was formulated and verified using contact area, interference, and stiffness of the couplings as the design variables.

A LEGO®-like coupling design was printed out of ABS on an Afinia H480 Fused Deposition Modeling (FDM) printer and measured with a ZEISS MICURA Coordinate Measuring Machine.

Varying interference, contact area, and stiffness had a statistically insignificant effect of accuracy.

Varying interference had a statistically insignificant effect on repeatability, increasing contact area increased repeatability by 0.75 [mu]m, or 15%, and increasing increased repeatability by 0.57 [mu]m, or 12%.

In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%.

We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges.

Significant development was completed on the GRCop-84 and GRCop-42 alloys in the extruded wrought form demonstrating feasibility for combustion chambers.

NASA has recently developed a process for additive manufacturing, specifically Powder Bed Fusion (PBF) or Selective Laser Melting (SLM), of GRCop-42 to establish parameters, characterize the material, and complete testing of components with complex internal features.

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