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Daiki is a PhD candidate at the University of Cambridge and currently working with Dr. Sebastian W. Pattinson in Complex Additive Materials group. His current research interests include 3D/4D printing technologies, computational design of architected materials and advanced structures, the confluence of artificial intelligence and mechanics to address societal challenges in biomedical applications and soft human-machine interfacing devices.
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.
Daiki Ikeuchi; Alejandro Vargas-Uscategui; Xiaofeng Wu; Peter King. Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing. Applied Sciences 2021, 11, 1654 .
AMA StyleDaiki Ikeuchi, Alejandro Vargas-Uscategui, Xiaofeng Wu, Peter King. Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing. Applied Sciences. 2021; 11 (4):1654.
Chicago/Turabian StyleDaiki Ikeuchi; Alejandro Vargas-Uscategui; Xiaofeng Wu; Peter King. 2021. "Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing." Applied Sciences 11, no. 4: 1654.
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.
Daiki Ikeuchi; Alejandro Vargas-Uscategui; Xiaofeng Wu; Peter C. King. Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing. Materials 2019, 12, 2827 .
AMA StyleDaiki Ikeuchi, Alejandro Vargas-Uscategui, Xiaofeng Wu, Peter C. King. Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing. Materials. 2019; 12 (17):2827.
Chicago/Turabian StyleDaiki Ikeuchi; Alejandro Vargas-Uscategui; Xiaofeng Wu; Peter C. King. 2019. "Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing." Materials 12, no. 17: 2827.
Although the Peltier sub-cooled trans-critical CO2 cycle concept has been applied for refrigeration, which typically involves discharging the heat into ambient air, this system is rarely considered for heat pumping purposes. Therefore, this research aims to expand the scope of the Peltier sub-cooled trans-critical CO2 cycle into heat pump water heating where the generated heat is uniquely discharged into water at temperatures progressively higher than ambient. The heat flows between the CO2 and flowing water are modelled as Nusselt based convective heat transfers where a 1D model is imposed to the direct gas cooler to improve simulation accuracy. Moreover, important but often neglected characteristics such as Peltier device size and Peltier heating factor (PHF) will also be analyzed. Results indicate that the PHF has an extremely strong influence on the overall system’s coefficient of performance (COP). Specifically, an optimal PHF value exists as a trade-off between the benefit of sub-cooling and the losses due to reduced CO2 mass flow rate, the latter of which caused reductions in the convective heat transfer coefficient and the direct gas cooler’s heating capacity. In the meantime, although larger Peltier device sizes improves the system COP, the improvement will converge towards a specific maximum.
Trevor Hocksun Kwan; Daiki Ikeuchi; Qinghe Yao. Application of the Peltier sub-cooled trans-critical carbon dioxide heat pump system for water heating – Modelling and performance analysis. Energy Conversion and Management 2019, 185, 574 -585.
AMA StyleTrevor Hocksun Kwan, Daiki Ikeuchi, Qinghe Yao. Application of the Peltier sub-cooled trans-critical carbon dioxide heat pump system for water heating – Modelling and performance analysis. Energy Conversion and Management. 2019; 185 ():574-585.
Chicago/Turabian StyleTrevor Hocksun Kwan; Daiki Ikeuchi; Qinghe Yao. 2019. "Application of the Peltier sub-cooled trans-critical carbon dioxide heat pump system for water heating – Modelling and performance analysis." Energy Conversion and Management 185, no. : 574-585.
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.
Daiki Ikeuchi; D.J.M. King; Kevin Laws; Alexander Knowles; R.D. Aughterson; G.R. Lumpkin; E.G. Obbard. Cr-Mo-V-W: A new refractory and transition metal high-entropy alloy system. Scripta Materialia 2019, 158, 141 -145.
AMA StyleDaiki Ikeuchi, D.J.M. King, Kevin Laws, Alexander Knowles, R.D. Aughterson, G.R. Lumpkin, E.G. Obbard. Cr-Mo-V-W: A new refractory and transition metal high-entropy alloy system. Scripta Materialia. 2019; 158 ():141-145.
Chicago/Turabian StyleDaiki Ikeuchi; D.J.M. King; Kevin Laws; Alexander Knowles; R.D. Aughterson; G.R. Lumpkin; E.G. Obbard. 2019. "Cr-Mo-V-W: A new refractory and transition metal high-entropy alloy system." Scripta Materialia 158, no. : 141-145.