ORCID Profile
0000-0002-6707-5969
Current Organisations
Institute for Manufacturing
,
University of Cambridge
,
CSIRO
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Publisher: Elsevier BV
Date: 04-2019
Publisher: MDPI AG
Date: 12-02-2021
DOI: 10.3390/APP11041654
Abstract: Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed, and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that a neural network model can outperform a widely used mathematical model with data-efficient modelling techniques and be better suited to improving geometric control in cold spray additive manufacturing.
Publisher: MDPI AG
Date: 12-2021
Abstract: This paper presents the design of a flexible bending actuator using shape memory alloy (SMA) and its integration in attitude control for solar sailing. The SMA actuator has advantages in its power-to-weight ratio and light weight. The bending mechanism and models of the actuator were designed and developed. A neural network based adaptive controller was implemented to control the non-linear nature of the SMA actuator. The actuator control modules were integrated into the solar sail attitude model with a quaternion PD controller that formed a cascade control. The feasibility and performance of the proposed actuator for attitude control were investigated and evaluated, showing that the actuator could generate 1.5 × 10−3 Nm torque which maneuvered a 1600 m2 CubeSat based solar sail by 45° in 14 h. The results demonstrate that the proposed SMA bending actuator can be effectively integrated in attitude control for solar sailing under moderate external disturbances using an appropriate controller design, indicating the potential of a lighter solar sail for future missions.
Publisher: MDPI AG
Date: 02-09-2019
DOI: 10.3390/MA12172827
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.
Publisher: Elsevier BV
Date: 2019
Publisher: ASM International
Date: 22-05-2023
DOI: 10.31399/ASM.CP.ITSC2023P0015
Abstract: Cold spray additive manufacturing is an emerging solid-state deposition process that enables large-scale components to be manufactured at high production rates. Control over geometry is important for reducing the development and growth of defects during the 3D build process and improving the final dimensional accuracy and quality of components. To this end, a machine learning approach has recently gained interest in modelling additively manufactured geometry however, such a data-driven modelling framework lacks the explicit consideration of a depositing surface and domain knowledge in cold spray additive manufacturing. Therefore, this study presents surface-aware data-driven modelling of an overlapping-track profile using a Gaussian Process Regression model. The proposed Gaussian Process modelling framework explicitly incorporated two relevant geometric features (i.e., surface type and polar length from the nozzle exit to the surface) and a widely adopted Gaussian superposing model as prior domain knowledge in the form of an explicit mean function. It was shown that the proposed model is able to provide better predictive performance than the Gaussian superposing model alone and purely data-driven Gaussian Process model, providing consistent overlapping-track profile predictions at all overlapping ratios. By combining accurate prediction of track geometry with toolpath planning, it is anticipated that improved geometric control and product quality can be achieved in cold spray additive manufacturing.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: Australia
Location: Australia
No related grants have been discovered for Daiki Ikeuchi.