« Back
Machine Learning Approach to Geometry Prediction in Cold Spray Additive Manufacturing
Poster Title: Machine Learning Approach to Geometry Prediction in Cold Spray Additive Manufacturing
Submitted on 18 Mar 2020
Author(s): Daiki Ikeuchi
Affiliations: The University of Sydney
This poster was presented at The Materials Innovations in Surface Engineering (MISE) Conference
Poster Views: 376
View poster »

Poster Information
Abstract: 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. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. 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. 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.Summary: This poster presents a summary of our work on "Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing" published as an open-access journal article.References: Full version (Open Access Journal) is available: abuse »
Creative Commons