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.
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.Tags: Heroes Qualities EssayInternational Case Studies In Asset Management By Chris LloydMla Research Paper Citation MachineGary Soto The Pie EssayThesis Vs AntithesisWriting Comparison Essays
more Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state.
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.
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.