- Institute for Manufacturing, 17 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom
Daiki Ikeuchi
University of Cambridge, Department of Engineering, Graduate Student
Cold spray additive manufacturing is an emerging solid-state deposition process that enables largescale components to be manufactured at high-production rates. Control over geometry is important for reducing the development and growth of... more
Cold spray additive manufacturing is an emerging solid-state deposition process that enables largescale 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 overlappingtrack 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 overlappingtrack 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.
Research Interests: Artificial Intelligence, Machine Learning, Manufacturing, Titanium, Thermal Spray Coating, and 9 moreAdditive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks, Geometry, Quality Assurance, Surface Coatings, Additive Manufacturing and 3D printing, Surface and Coatings Technology, and Cold Spray technology and applications
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... more
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 m^2 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.
Research Interests:
Open Access Article: https://doi.org/10.3390/app11041654 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... more
Open Access Article: https://doi.org/10.3390/app11041654
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 tech-nology'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.
Related works:
(Open Access) Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing - https://doi.org/10.3390/ma12172827
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 tech-nology'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.
Related works:
(Open Access) Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing - https://doi.org/10.3390/ma12172827
Research Interests: Machine Learning, Manufacturing, Back Propagation, Titanium, Neural Networks, and 9 moreModeling and Simulation, Industrial Engineering, Additive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks, Laser Cladding, Dimensional accuracy, Cold Spray technology and applications, and Wire Arc Additive Manufacturing
Open Access Article: https://doi.org/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... more
Open Access Article:
https://doi.org/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.
Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
https://doi.org/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.
Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
Research Interests: Manufacturing, Back Propagation, Titanium, Neural Networks, Modeling and Simulation, and 13 moreIndustrial Engineering, Thermal Spray Coating, 3D printing, Additive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks, Welding, Additive Manufacturing and 3D printing, Tool Path, Laser Cladding, Dimensional accuracy, Cold Spray, and Wire Arc Additive Manufacturing
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,... more
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.
https://doi.org/10.1016/j.enconman.2019.01.104
https://doi.org/10.1016/j.enconman.2019.01.104
Research Interests:
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... more
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.
https://doi.org/10.1016/j.scriptamat.2018.08.045
https://doi.org/10.1016/j.scriptamat.2018.08.045
Research Interests: Microstructure, Scanning Electron Microscopy, Transmission Electron Microscopy, Materials Science and Engineering, X-ray diffraction (Materials Characterisation), and 12 moreHigh Entropy Alloys, Chromium, Molybdenum, Materials Simulation and Modeling, Vanadium, Computational Thermodynamics, CALPHAD, Tungsten, Density Functional Theory (DFT), Refractory Metals, High Entropy Alloy, High Entropy Materials, and Alloy Search and Predict (ASAP)
Increased accessibility to additive manufacturing technology facilitates democratization of manufacturing, bringing it to habitable environments. The operation of additive manufacturing can be hazardous to human health mid-long term.... more
Increased accessibility to additive manufacturing technology facilitates democratization of manufacturing, bringing it to habitable environments. The operation of additive manufacturing can be hazardous to human health mid-long term. Virtual sensing extends the capabilities of hardware sensors enabling affordable monitoring to ensure safe operation in democratized manufacturing environments. However, the development process has not yet been standardized for informally trained personnel to facilitate the adoption of virtual sensors. This paper presents a case study analysis to propose a standardized process for the data collection and development of virtual sensors for indoor air quality monitoring in democratized manufacturing environments.
Research Interests:
Human exposure to poor air quality is a leading risk factor in the Global Burden of Disease (GBD) study, estimating 22,000 premature deaths related to indoor air pollution in 2019 in Europe. Diverse pollutants are found in manufacturing... more
Human exposure to poor air quality is a leading risk factor in the Global Burden of Disease (GBD) study, estimating 22,000 premature deaths related to indoor air pollution in 2019 in Europe. Diverse pollutants are found in manufacturing environments resulting from both combustion and non-combustion sources, including Particulate Matter and Volatile Organic Compound. Internet of Things (IoT) air quality monitoring can enhance awareness and support informed decision making towards better air quality. However, hardware sensors are not always capable of monitoring particular characteristics and behaviour of a pollutant, for instance, spatial limitations may impede deploying sensors close enough to the source of the pollutant. Virtual Sensors can extend hardware sensing options via signal processing and data integration. This paper presents an architecture for training and deploying virtual sensors. A virtual sensor is implemented using the architecture in the context of additive manufacturing to estimate the production of Volatile Organic Compounds (VOCs) of 3D printers and their transfer into the rest of the space. In the case study, the 3D printers are installed inside cabinets to limit the transfer of pollutants to the exterior. Several of these virtual sensors are deployed to monitor the VOCs produced by the 3D printers and the transfer rate out of the cabinets. The paper includes some early results and initial insights on the accuracy and usefulness of virtual sensors. Virtual Sensors can be cost-effective solutions when monitoring systems are escalated by reducing number of hardware sensors and complexity.
Research Interests:
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... more
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.
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.
Research Interests:
Human exposure to poor air quality is a leading risk factor in the Global Burden of Disease (GBD) study, estimating 22,000 premature deaths related to indoor air pollution in 2019 in Europe. Diverse pollutants are found in manufacturing... more
Human exposure to poor air quality is a leading risk factor in the Global Burden of Disease (GBD) study, estimating 22,000 premature deaths related to indoor air pollution in 2019 in Europe. Diverse pollutants are found in manufacturing environments resulting from both combustion and non-combustion sources, including Particulate Matter and Volatile Organic Compound. Internet of Things (IoT) air quality monitoring can enhance awareness and support informed decision making towards better air quality. However, hardware sensors are not always capable of monitoring particular characteristics and behaviour of a pollutant, for instance, spatial limitations may impede deploying sensors close enough to the source of the pollutant. Virtual Sensors can extend hardware sensing options via signal processing and data integration. This paper presents an architecture for training and deploying virtual sensors. A virtual sensor is implemented using the architecture in the context of additive manufacturing to estimate the production of Volatile Organic Compounds (VOCs) of 3D printers and their transfer into the rest of the space. In the case study, the 3D printers are installed inside cabinets to limit the transfer of pollutants to the exterior. Several of these virtual sensors are deployed to monitor the VOCs produced by the 3D printers and the transfer rate out of the cabinets. The paper includes some early results and initial insights on the accuracy and usefulness of virtual sensors. Virtual Sensors can be cost-effective solutions when monitoring systems are escalated by reducing number of hardware sensors and complexity.
Research Interests:
We develop visionary software capable of producing toolpaths from a CAD file for high throughput robotic additive manufacturing with continuous supply of powdered feedstock. The toolpath planning strategy uses minimal turns and an angled... more
We develop visionary software capable of producing toolpaths from a CAD file for high throughput robotic additive manufacturing with continuous supply of powdered feedstock. The toolpath planning strategy uses minimal turns and an angled tool on contour paths to build straight walls in a multi-robot coordinated movement.
Research Interests:
Cold spray additive manufacturing is an emerging technology that offers unique advantages, including high production rate, unlimited product size and the ability to process oxygen-sensitive materials. However, dimensional control and... more
Cold spray additive manufacturing is an emerging technology that offers unique advantages, including high production rate, unlimited product size and the ability to process oxygen-sensitive materials. However, dimensional control and accuracy in cold spray additive manufacturing are challenging, which limits its integration into commercial manufacturing systems. These problems originate from the poor understanding of the complex relationship between process parameters and the resulting deposit geometry. This knowledge gap motivated the development of an
accurate predictive model for the geometry of a cold spray deposit profile to overcome the problems. Recently, a machine learning approach has gained interest in developing the predictive model of such a complex additive manufacturing process due to its superior nonlinear mapping capability, as seen in other manufacturing applications. Nevertheless, such a mapping capability can be realised only with a large amount of experimental data which is often impractical to collect in additive manufacturing applications. This data-scarcity issue has motivated the exploration of a data-efficient machine learning approach suitable for complex process modelling with limited data. Therefore, the objective of this study was to investigate a data-efficient machine learning approach to geometry prediction in cold spray additive manufacturing. The proposed modelling approach incorporated a conventional Gaussian mathematical model into the development and learning process of a data-driven model. We compared to purely mathematical and data-driven modelling results and showed that the proposed modelling approach provided improved predictive accuracy. The findings can contribute to the control and optimisation of the process for shorter production time and the development of build strategy for better as-fabricated surface and dimensional quality control. The approach in this study is also applicable in other deposition-based additive manufacturing technologies such as Wire and Arc Additive Manufacturing.
accurate predictive model for the geometry of a cold spray deposit profile to overcome the problems. Recently, a machine learning approach has gained interest in developing the predictive model of such a complex additive manufacturing process due to its superior nonlinear mapping capability, as seen in other manufacturing applications. Nevertheless, such a mapping capability can be realised only with a large amount of experimental data which is often impractical to collect in additive manufacturing applications. This data-scarcity issue has motivated the exploration of a data-efficient machine learning approach suitable for complex process modelling with limited data. Therefore, the objective of this study was to investigate a data-efficient machine learning approach to geometry prediction in cold spray additive manufacturing. The proposed modelling approach incorporated a conventional Gaussian mathematical model into the development and learning process of a data-driven model. We compared to purely mathematical and data-driven modelling results and showed that the proposed modelling approach provided improved predictive accuracy. The findings can contribute to the control and optimisation of the process for shorter production time and the development of build strategy for better as-fabricated surface and dimensional quality control. The approach in this study is also applicable in other deposition-based additive manufacturing technologies such as Wire and Arc Additive Manufacturing.
Research Interests: Machine Learning, Titanium, Thermal Spray Coating, 3D printing, Additive Manufacturing, and 7 moreCold Gas Dynamic Spraying, Artificial Neural Networks, Gaussian processes, Surface Coatings, Additive Manufacturing and 3D printing, Surface and Coatings Technology, and Cold Spray technology and applications
Cold spray additive manufacturing is an emerging technology that offers unique advantages, including high production rate, unlimited product size and the ability to process oxygen-sensitive materials. However, dimensional control and... more
Cold spray additive manufacturing is an emerging technology that offers unique advantages, including high production rate, unlimited product size and the ability to process oxygen-sensitive materials. However, dimensional control and accuracy in cold spray additive manufacturing are challenging, which limits its integration into commercial manufacturing systems. These problems originate from the poor understanding of the complex relationship between process parameters and the resulting fabricated geometry. This knowledge gap motivated the development of an accurate predictive model for the geometry of a cold spray track profile to overcome the problems. Recently, a machine learning approach has gained interest in developing the predictive model of such a complex additive manufacturing process due to its superior nonlinear mapping capability, as seen in other manufacturing applications. Nevertheless, such a mapping capability can be realised only with a large amount of experimental data which is often impractical to collect in additive manufacturing applications. This limited data issue has motivated the exploration of a data-efficient machine learning approach suitable for complex process modelling with limited data. Therefore, the objective of this study was to investigate a data-efficient machine learning approach to geometry prediction in cold spray additive manufacturing. The proposed approach was of hybrid modelling framework, incorporating a conventional mathematical Gaussian model into the development and learning process of a data-driven model. We compared to purely mathematical Gaussian and data-driven modelling results and showed that the proposed hybrid modelling approach provided improved predictive accuracy. The findings can contribute to the control and optimisation of the process for shorter production time and the development of build strategy for better as-fabricated surface and dimensional quality control. The approach in this study is also applicable in other deposition-based additive manufacturing technologies such as Wire and Arc Additive Manufacturing.
For Further Information:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
Our Related works:
(Open Access) Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing - https://doi.org/10.3390/ma12172827
For Further Information:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
Our Related works:
(Open Access) Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing - https://doi.org/10.3390/ma12172827
Research Interests: Titanium, Neural Networks, Additive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks for modeling purposes, and 8 moreArtificial Neural Networks, Surface Coatings, Additive Manufacturing and 3D printing, Surface and Coatings Technology, Laser Cladding, Cold Spray technology and applications, Cold Spray, and Wire Arc Additive Manufacturing
Full Open Access Journal Version Available: https://doi.org/10.3390/ma12172827, Abstract: Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture... more
Full Open Access Journal Version Available:
https://doi.org/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.
Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
(Open Access) Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing - https://doi.org/10.3390/ma12172827
https://doi.org/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.
Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
(Open Access) Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing - https://doi.org/10.3390/ma12172827
Research Interests: Manufacturing, Back Propagation, Titanium, Neural Networks, Modeling and Simulation, and 11 moreIndustrial Engineering, Thermal Spray Coating, 3D printing, Neural Network, Additive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks for modeling purposes, Artificial Neural Networks, Welding, Additive Manufacturing and 3D printing, and Cold Spray technology and applications
Full journal version available: doi: 10.1016/j.scriptamat.2018.08.045 This poster presents the preliminary results of our work on Cr-Mo-V-W high-entropy alloy published as "Cr-Mo-V-W: A new refractory and transition metal high-entropy... more
Full journal version available: doi: 10.1016/j.scriptamat.2018.08.045
This poster presents the preliminary results of our work on Cr-Mo-V-W high-entropy alloy published as "Cr-Mo-V-W: A new refractory and transition metal high-entropy alloy system".
For further details, http://unsworks.unsw.edu.au/fapi/datastream/unsworks:57273/bin5cf5882d-409c-485b-b03d-3190d0c72699?view=true
Abstract: 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.
DOI: 10.1016/j.scriptamat.2018.08.045
This poster presents the preliminary results of our work on Cr-Mo-V-W high-entropy alloy published as "Cr-Mo-V-W: A new refractory and transition metal high-entropy alloy system".
For further details, http://unsworks.unsw.edu.au/fapi/datastream/unsworks:57273/bin5cf5882d-409c-485b-b03d-3190d0c72699?view=true
Abstract: 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.
DOI: 10.1016/j.scriptamat.2018.08.045
Research Interests: Materials Science, Scanning Electron Microscopy, Transmission Electron Microscopy, Density-functional theory, Materials Science and Engineering, and 10 moreX-ray Diffraction, Transition metals, High Entropy Alloys, Chromium, Molybdenum, Refractory materials reseach, Vanadium, Computational Thermodynamics, CALPHAD, Tungsten, and High Entropy Alloy
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:... more
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.
Research Interests:
A shape memory alloy actuator is widely used in various engineering fields due to its large force-to-weight ratio, large displacement, compact size and noiseless operation. However, the use of the actuator still remains uncommon compared... more
A shape memory alloy actuator is widely used in various engineering fields due to its large force-to-weight ratio, large displacement, compact size and noiseless operation. However, the use of the actuator still remains uncommon compared with conventional actuators, especially as an active actuator. One of the reasons for such observation is attributed to the control difficulty due to the nonlinear and hysteresis nature of shape memory alloy and limited state measurement capabilities. Despite the great amount of research on the nonlinear and hysteresis sides, there has not been much research focus on the improvement of uncertainty associated with the lack of measured state variables. In this thesis, the author proposes to design the Dual Unscented Kalman Filter model-based state and parameter estimator for a simple spring-biased SMA wire actuator and investigate the numerical feasibility of the estimation algorithms to overcome the aforementioned control issue. The findings of this study are expected to contribute to the shape memory alloy actuator control community to raise the awareness of significance of mitigating state and parameter uncertainties in terms of high-precision control through the use of estimation algorithms.
DOI: 10.13140/RG.2.2.25892.22402
DOI: 10.13140/RG.2.2.25892.22402
Research Interests:
制御仕様書からSimulinkモデル作成に必要なスキルを学ぶための問題集.
ISBN: 978-4-907963-01-9
JP番号: 22903922
Purchase from:
http://www.int-tech.co.jp/work_seigyojyuku.html
https://iss.ndl.go.jp/books/R100000002-I028288437-00
ISBN: 978-4-907963-01-9
JP番号: 22903922
Purchase from:
http://www.int-tech.co.jp/work_seigyojyuku.html
https://iss.ndl.go.jp/books/R100000002-I028288437-00
Research Interests:
Predictive Functional Control is a type of modern control algorithms stemming its origin from Model Predictive Control and carrying the potential to replace conventional PID controllers and its variants in many engineering industries. At... more
Predictive Functional Control is a type of modern control algorithms stemming its origin from Model Predictive Control and carrying the potential to replace conventional PID controllers and its variants in many engineering industries. At low-level control stages, it is often necessary to utilize some of the characteristics of Model Predictive Control such as input constraint handling and offset free tracking. In this paper, we study the characteristics of Predictive Functional Control and implement the algorithm with application to automobile actuator (i.e. first-order servomotor).
ISBN: 978-4-907963-02-6
Purchase from http://www.int-tech.co.jp/work_seigyojyuku.html
The citation command is as follows:
@inbook{Ikeuchi2019,
Author = {Ikeuchi, Daiki and Fukuda, Shinnosuke and Sakaguchi, Mitsuru and Nagata, Shingo and Funada, Hiroyoshi},
Chapter = {4},
Edition = {1},
Month = {July},
Pages = {22-24},
Publisher = {Integration Technology Co., Ltd.},
Title = {Predictive Functional Control with Application to Automobile Actuators},
Volume = {1},
Year = {2019}}
ISBN: 978-4-907963-02-6
Purchase from http://www.int-tech.co.jp/work_seigyojyuku.html
The citation command is as follows:
@inbook{Ikeuchi2019,
Author = {Ikeuchi, Daiki and Fukuda, Shinnosuke and Sakaguchi, Mitsuru and Nagata, Shingo and Funada, Hiroyoshi},
Chapter = {4},
Edition = {1},
Month = {July},
Pages = {22-24},
Publisher = {Integration Technology Co., Ltd.},
Title = {Predictive Functional Control with Application to Automobile Actuators},
Volume = {1},
Year = {2019}}
Research Interests:
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... more
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.
Research Interests:
Human exposure to poor air quality is a leading risk factor in the Global Burden of Disease (GBD) study, estimating 22,000 premature deaths related to indoor air pollution in 2019 in Europe. Diverse pollutants are found in manufacturing... more
Human exposure to poor air quality is a leading risk factor in the Global Burden of Disease (GBD) study, estimating 22,000 premature deaths related to indoor air pollution in 2019 in Europe. Diverse pollutants are found in manufacturing environments resulting from both combustion and non-combustion sources, including Particulate Matter and Volatile Organic Compound. Internet of Things (IoT) air quality monitoring can enhance awareness and support informed decision making towards better air quality. However, hardware sensors are not always capable of monitoring particular characteristics and behaviour of a pollutant, for instance, spatial limitations may impede deploying sensors close enough to the source of the pollutant. Virtual Sensors can extend hardware sensing options via signal processing and data integration. This paper presents an architecture for training and deploying virtual sensors. A virtual sensor is implemented using the architecture in the context of additive manufacturing to estimate the production of Volatile Organic Compounds (VOCs) of 3D printers and their transfer into the rest of the space. In the case study, the 3D printers are installed inside cabinets to limit the transfer of pollutants to the exterior. Several of these virtual sensors are deployed to monitor the VOCs produced by the 3D printers and the transfer rate out of the cabinets. The paper includes some early results and initial insights on the accuracy and usefulness of virtual sensors. Virtual Sensors can be cost-effective solutions when monitoring systems are escalated by reducing number of hardware sensors and complexity.
Research Interests: Air Quality, Machine Learning, Indoor Air Quality, 3D printing, Particulate Matter Emissions, and 8 moreVirtual Sensors, Virtual sensing, Soft Sensors, Additive Manufacturing and 3D printing, Indoor air pollution, Particulate Matter, Air Quality Monitoring and Modelling, and Volatile organic compound
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... more
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 m^2 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.
Research Interests:
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 tech-nology's industrial maturity, the problem of... more
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 tech-nology'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.
Research Interests: Mechanical Engineering, Machine Learning, Titanium, Neural Networks, Thermal Spray Coating, and 14 moreAdditive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks for modeling purposes, Artificial Neural Networks, Gaussian processes, Applied Sciences, 3-D printing, Thermal spray, The University of Sydney, Laser Cladding, Cold Spray technology and applications, Cold Spray, metal additive manufacturing, and Waam
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 tech-nology's industrial maturity, the problem of geometric... more
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 tech-nology'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.
Research Interests: Computer Science, Machine Learning, Manufacturing, Back Propagation, Titanium, and 12 moreNeural Networks, Modeling and Simulation, Industrial Engineering, Additive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks, Applied Sciences, Artificial Neural Network, Laser Cladding, Dimensional accuracy, Cold Spray technology and applications, and Wire Arc Additive Manufacturing
Abstract 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.... more
Abstract 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.
Research Interests: Nuclear Engineering, Materials Science, Thermodynamics, Performance Studies, Carbon Dioxide, and 14 moreEnergy, Heat Transfer, Modeling and Simulation, Heat pump, Water Heating, Thermodynamic analysis, Convective Heat Transfer, Heat pump water Heater, Electrical And Electronic Engineering, Thermoelectric effect, Transcritical Flow, Peltier device, Coefficient of Performance (COP), and Subcooling
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... more
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.
Research Interests: Materials Engineering, Mechanical Engineering, Materials Science, Design, Microstructure, and 15 moreMetallurgy, Materials, Scanning Electron Microscopy, Materials Science and Engineering, High Entropy Alloys, Multicomponent Alloys, Chromium, Molybdenum, Materials Simulation and Modeling, Alloy, Phase, Oxidation, Refractory Metals, High Entropy Alloy, and High Entropy Materials
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... 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. 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.
Research Interests: Engineering, Computer Science, Manufacturing, Back Propagation, Materials, and 15 moreTitanium, Neural Networks, Modeling and Simulation, Industrial Engineering, Thermal Spray Coating, Medicine, Additive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks, CHEMICAL SCIENCES, Tool Path, Artificial Neural Network, Laser Cladding, Dimensional accuracy, and Cold Spray
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... more
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.
Research Interests:
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 tech-nology's industrial maturity, the problem of geometric... more
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 tech-nology'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.
Research Interests:
A shape memory alloy actuator is widely used in various engineering fields due to its large force-to-weight ratio, large displacement, compact size and noiseless operation. However, the use of the actuator still remains uncommon compared... more
A shape memory alloy actuator is widely used in various engineering fields due to its large force-to-weight ratio, large displacement, compact size and noiseless operation. However, the use of the actuator still remains uncommon compared with conventional actuators, especially as an active actuator. One of the reasons for such observation is attributed to the control difficulty due to the nonlinear and hysteresis nature of shape memory alloy and limited state measurement capabilities. Despite the great amount of research on the nonlinear and hysteresis sides, there has not been much research focus on the improvement of uncertainty associated with the lack of measured state variables. In this thesis, the author proposes to design the Dual Unscented Kalman Filter model-based state and parameter estimator for a simple spring-biased SMA wire actuator and investigate the numerical feasibility of the estimation algorithms to overcome the aforementioned control issue. The findings of this study are expected to contribute to the shape memory alloy actuator control community to raise the awareness of significance of mitigating state and parameter uncertainties in terms of high-precision control through the use of estimation algorithms. DOI: 10.13140/RG.2.2.25892.22402
Research Interests:
Full journal version available: doi: 10.1016/j.scriptamat.2018.08.045 This poster presents the preliminary results of our work on Cr-Mo-V-W high-entropy alloy published as "Cr-Mo-V-W: A new refractory and transition metal high-entropy... more
Full journal version available: doi: 10.1016/j.scriptamat.2018.08.045 This poster presents the preliminary results of our work on Cr-Mo-V-W high-entropy alloy published as "Cr-Mo-V-W: A new refractory and transition metal high-entropy alloy system". For further details, http://unsworks.unsw.edu.au/fapi/datastream/unsworks:57273/bin5cf5882d-409c-485b-b03d-3190d0c72699?view=true Abstract: 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 micro segregation was verified. DOI: 10.1016/j.scriptamat.2018.08.045
Research Interests: Materials Science, Scanning Electron Microscopy, Tungsten and Its Alloys, Transmission Electron Microscopy, Density-functional theory, and 13 moreMaterials Science and Engineering, X-ray Diffraction, Density Functional Theory, Transition metals, High Entropy Alloys, Chromium, Molybdenum, Vanadium, Computational Thermodynamics, CALPHAD, Tungsten, Density Functional Theory (DFT), High Entropy Alloy, and refractory high entropy alloys
Research Interests: Materials Engineering, Mechanical Engineering, Materials Science, Design, Microstructure, and 15 moreMaterials, Scanning Electron Microscopy, Materials Science and Engineering, High Entropy Alloys, Multicomponent Alloys, Chromium, Molybdenum, Materials Simulation and Modeling, Alloy, Phase, Science Technology, Oxidation, Refractory Metals, High Entropy Alloy, and High Entropy Materials
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... more
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 co...
Research Interests:
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... more
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...
Research Interests: Computer Science, Machine Learning, Manufacturing, Back Propagation, Titanium, and 11 moreNeural Networks, Modeling and Simulation, Industrial Engineering, Additive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks, Applied Sciences, Laser Cladding, Dimensional accuracy, Cold Spray technology and applications, and Wire Arc Additive Manufacturing
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,... more
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.
Research Interests: Thermodynamics, Performance Studies, Carbon Dioxide, Energy, Heat Transfer, and 11 moreModeling and Simulation, Heat pump, Water Heating, Thermodynamic analysis, Convective Heat Transfer, Heat pump water Heater, Electrical And Electronic Engineering, Transcritical Flow, Peltier device, Coefficient of Performance (COP), and Subcooling
Open Access Article: https://doi.org/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... more
Open Access Article:
https://doi.org/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.
Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
https://doi.org/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.
Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
Research Interests: Engineering, Manufacturing, Back Propagation, Materials, Titanium, and 15 moreNeural Networks, Modeling and Simulation, Industrial Engineering, Thermal Spray Coating, Additive Manufacturing, Cold Gas Dynamic Spraying, Artificial Neural Networks, Welding, CHEMICAL SCIENCES, 3-D printing, Tool Path, Laser Cladding, Dimensional accuracy, Cold Spray, and Wire Arc Additive Manufacturing
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,... more
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.
Research Interests: Thermodynamics, Performance Studies, Carbon Dioxide, Energy, Heat Transfer, and 11 moreModeling and Simulation, Heat pump, Water Heating, Thermodynamic analysis, Convective Heat Transfer, Heat pump water Heater, Electrical And Electronic Engineering, Transcritical Flow, Peltier device, Coefficient of Performance (COP), and Subcooling
Open Access Article: https://doi.org/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... more
Open Access Article:
https://doi.org/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.
Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654
https://doi.org/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.
Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654