SYNTHESES AND PROPERTIES OF COPPER HYDROXIDE NANOSHEETS AND CONTROLLED DEPOSITION

In this study, we synthesized copper hydroxide nanosheet and investigated its electrochemical property and how to deposit it with a uniform amount. The precursor of the nanosheet was a layered copper hydroxide synthesized by the ion exchange of dodecylbenzene sulfonate with acetate in Cu2(OH)3(CH3COO)·H2O. The nanosheet was prepared by delamination of the layered copper hydroxide by dispersion in 1-butanol. Atomic force microscopy images of the nanosheets showed lateral dimensions of ca. 2 μm with the height of ca. 4.5 nm. Cyclic voltammogram of the nanosheet in basic solution showed two cathodic peaks and two anodic peaks similar to copper oxide electrode. To deposit the nanosheet, a quartz glass slide was dipped in the dispersion of the nanosheet in 1-butanol and dried after washing. This procedure was repeated and the ultraviolet and visible light absorption spectrum of the slide was measured. The absorbance of the slide increased in direct proportion to the number of times of the dip-and-dry procedure. Thus we confirmed that controlled amount of nanosheet was deposited on the quartz glass

Data-Driven Overlapping-Track Profile Modeling in Cold Spray Additive Manufacturing

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 modeling additively manufactured geometry; however, such a data-driven modeling 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 modeling of an overlapping-track profile using a Gaussian Process Regression model. The proposed Gaussian Process modeling 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 could provide better predictive performance than the Gaussian superposing model alone and the 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

Cr-Mo-V-W: A new refractory and transition metal high-entropy alloy system

Cr-Mo-V-W high-entropy alloy (HEA) is studied, with 2553 K equilibrium solidus and high Cr content to promote protective oxide scale formation, suggesting potential applications in hot, oxidising environments. Alloy Search and Predict (ASAP) and phase diagram calculations found a single phase, body-centred cubic (BCC) solid solution at elevated temperatures, across the range of compositions present within the system - uncommon for a HEA of refractory and transition metals. Density functional theory identified solubility of 22 at.% Cr at solidus temperature, with composition-dependent drive for segregation during cooling. An as-cast, BCC single-phase with the composition 31.3Cr-23.6Mo-26.4 V-18.7 W exhibiting dendritic microsegregation was verified

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing

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

Data-efficient Machine Learning for Geometry Modelling in Cold Spray Additive Manufacturing

Additive manufacturing (AM) is widely recognised as a paradigm shift in the nature of future manufacturing as it has demonstrated the potential to offer the variety of benefits that are difficult to achieve otherwise, including: mass-customisation, great freedom of design, waste minimisation and the ability to fabricate complex shapes. However, the commercial integration of AM technologies is still greatly limited, especially High Production Rate AM (HPRAM) technology in metal AM domains. Such limitation is attributed to the lack of process monitoring and quality assurance measures in metal AM, indicating that quality control is a challenge to overcome. One such quality characteristic is geometry accuracy and consistency due to the AM processes being fundamentally complex with numerous process variables and the limited process modelling capabilities. Recently, data-driven machine learning modelling has attracted increasing attention due to its modelling capability without complete physical AM process insight. The downside of purely data-driven machine learning is the necessity of large volume of data for adequate predictive accuracy. This disadvantage is frequently encountered in industrial AM scenarios with foreseen customised and short-run productions, the high-value nature and process monitoring difficulty. The work presented in this thesis explores data-efficient machine learning for the modelling of macro-scale geometric deposit formation in one of HPRAM processes, Cold Spray AM. Herein, single- and overlapping-track cases are focused to demonstrate the proposed modelling approaches. The significance of this thesis is mainly three-fold: (1) exploring the data-driven machine learning modelling approach beyond its current AM use, (2) proposing data-efficient machine learning approach and compare to mathematical and purely data-driven approaches and (3) leveraging existing AM domain knowledge or previously proposed mathematical models to achieve data-efficiency

Data-Driven Overlapping Track Profile Modelling in Cold Spray Additive Manufacturing

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.</jats:p

Data-Driven Overlapping-Track Profile Modeling in Cold Spray Additive Manufacturing

Acknowledgements: This work was supported by CSIRO’s Active Integrated Matter Future Science Platform (AIM-FSP). Daiki Ikeuchi received Research Training Program (International) Scholarship from the Department of Education, Skills and Employment, Australia. The authors thank CSIRO Characterization for access to LEXT OLS4000 conformal laser scanning microscope.Funder: Commonwealth Scientific and Industrial Research Organisation (CSIRO)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 modeling additively manufactured geometry; however, such a data-driven modeling 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 modeling of an overlapping-track profile using a Gaussian Process Regression model. The proposed Gaussian Process modeling 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 could provide better predictive performance than the Gaussian superposing model alone and the 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

Design and Simulation of a Flexible Bending Actuator for Solar Sail Attitude Control

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 &times; 10&minus;3 Nm torque which maneuvered a 1600 m2 CubeSat based solar sail by 45&deg; 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

beta-Selective Glycosylation Using Axial-Rich and 2-O-Rhamnosylated Glucosyl Donors Controlled by the Protecting Pattern of the Second Sugar

Herein, we describe two counterexamples of the previously reported beta/alpha-selectivity of 96/4 for glycosylation using ethyl 2-O-[2,3,4-tris-O-ter(-butyldimethylsilyl (TBS)-alpha-L-rhamnopyranosyl]-3,4,6-tris-O-TBS-thio-beta-D-glucopyranoside as the glycosyl donor. Furthermore, we investigated the effects of protecting group on the rhamnose moieties in the glycosylation with cholestanol and revealed that beta-selectivity originated from the two TBS groups at the 3-O and 4-O positions of rhamnose. In contrast, the TBS group at the 2-O position of rhamnose hampered the beta-selectivity. Finally, the beta/alpha-selectivity during the glycosylation was enhanced to >= 99/1. The results obtained herein suggest that the protecting groups on the sugar connected to the 2-O of a glycosyl donor with axial-rich conformation can control the stereoselectivity of glycosylation