Intelligent engineering coatings for in-manufacture and in-service monitoring of critical safety products (CoatIN)
Lead Research Organisation:
University of Manchester
Department Name: Materials
Abstract
Coatings are key to the performance of most manufactured products and they contribute to sustainability by enhancing the efficiency and extending the life of the products that they protect, as well as by enabling the reduced use of scarce bulk materials. Coatings are a vital part of the nation's manufacturing industry, contributing to many sectors, including aerospace, energy, automotive, construction and healthcare. However, until now the UK coatings industry has been lagging behind compared to High Value Manufacturing sectors in terms of design, development, manufacture, and implementation into products, particularly in terms of the degree of digitalisation achieved.
This project will develop intelligent coatings with new functionalities that can self-monitor during manufacture and in-service. The multidisciplinary team will use a suite of modelling tools, sensing technologies and experimental deposition and characterisation facilities at The University of Manchester, the University of Sheffield, Cranfield University, Queen's University Belfast and Swansea University to embed new sensor functionalities within coatings, using a variety of deposition processes.
This EPSRC Exploratory Stream project will be a key step to address the challenges of digitalisation in the UK coatings manufacturing sector. The proposed research will assist UK PLC to develop manufacturing methods which are predictable, digital-enabled and more productive, providing a pathway to world-leading coating manufacturing processes. The research will support the coatings manufacturing industry to achieve best-in-class levels of High Value Manufacturing.
This project will develop intelligent coatings with new functionalities that can self-monitor during manufacture and in-service. The multidisciplinary team will use a suite of modelling tools, sensing technologies and experimental deposition and characterisation facilities at The University of Manchester, the University of Sheffield, Cranfield University, Queen's University Belfast and Swansea University to embed new sensor functionalities within coatings, using a variety of deposition processes.
This EPSRC Exploratory Stream project will be a key step to address the challenges of digitalisation in the UK coatings manufacturing sector. The proposed research will assist UK PLC to develop manufacturing methods which are predictable, digital-enabled and more productive, providing a pathway to world-leading coating manufacturing processes. The research will support the coatings manufacturing industry to achieve best-in-class levels of High Value Manufacturing.
Planned Impact
Recent studies (such as the one by the industry-led Special Interest Group in Surface Engineering and Advanced Coatings) have demonstrated that the UK coatings market is worth about £11 billion annually. Moreover, coatings are vital to over £150bn of products made in the UK each year, and these products drive manufacturing turnovers greater than £380bn in UK companies. Nevertheless, the coatings sector in the UK lags behind other manufacturing process technology sectors, particularly with regard to the level of digitalisation achieved. Such digitalisation capability is needed for coatings to achieve their full potential to add value to products and to contribute optimally to sustainability objectives, given the critical role which coatings can play in conserving energy and materials resources.
This proposal will challenge the traditional mode of coating manufacture by extending the application of coatings beyond a simple protective layer to a functional sensing element, with a capability to monitor and regulate the coating growth process as well as, using the same sensing method, measure the functional performance of the component in-service. This project will establish a new way of thinking that will transform the way coatings are seen in the life-cycle of an engineering component. Establishing coatings as sensing elements that allow not only monitoring and gathering of the deposition process data but also continue to function in-service as an integrated, full life-cycle, cyber-physical devices is a step-change in transformational digital manufacturing and connectivity of safety critical engineering components. This proposal will address the critical need to eliminate waste in coating manufacturing, which accounts for significant productivity losses in UK businesses, and increases costs and lead times of engineering components.
This project will provide the vital first step in the journey towards digital transformation of the UK coatings industry, which will result in improved manufacturing quality, productivity, efficiency and sustainability. Through digitalisation, the coatings industry sector will be able to faster develop novel coatings with improved performance and functionalities directed by end-user needs. Since coatings are critical to the performance of most products, the impact of this project will expand to many sectors in UK industry, ranging from aerospace and automotive to healthcare and food & drink. This project addresses the outcomes of the recent "Made Smarter" review, which estimated that adoption of digitalistion throughout UK industry could add up to £455 billion for UK manufacturing over the next decade, by increasing manufacturing sector growth between 1.5% and 3% per annum. The successful implementation of digitalisation technologies in the UK coatings industry will contribute to making the UK a global leader in industrial digitalisation in the coatings sector.
This proposal will challenge the traditional mode of coating manufacture by extending the application of coatings beyond a simple protective layer to a functional sensing element, with a capability to monitor and regulate the coating growth process as well as, using the same sensing method, measure the functional performance of the component in-service. This project will establish a new way of thinking that will transform the way coatings are seen in the life-cycle of an engineering component. Establishing coatings as sensing elements that allow not only monitoring and gathering of the deposition process data but also continue to function in-service as an integrated, full life-cycle, cyber-physical devices is a step-change in transformational digital manufacturing and connectivity of safety critical engineering components. This proposal will address the critical need to eliminate waste in coating manufacturing, which accounts for significant productivity losses in UK businesses, and increases costs and lead times of engineering components.
This project will provide the vital first step in the journey towards digital transformation of the UK coatings industry, which will result in improved manufacturing quality, productivity, efficiency and sustainability. Through digitalisation, the coatings industry sector will be able to faster develop novel coatings with improved performance and functionalities directed by end-user needs. Since coatings are critical to the performance of most products, the impact of this project will expand to many sectors in UK industry, ranging from aerospace and automotive to healthcare and food & drink. This project addresses the outcomes of the recent "Made Smarter" review, which estimated that adoption of digitalistion throughout UK industry could add up to £455 billion for UK manufacturing over the next decade, by increasing manufacturing sector growth between 1.5% and 3% per annum. The successful implementation of digitalisation technologies in the UK coatings industry will contribute to making the UK a global leader in industrial digitalisation in the coatings sector.
Organisations
- University of Manchester (Lead Research Organisation)
- University of Twente (Collaboration)
- TRL9 Limited (Project Partner)
- Qioptiq Limited (Project Partner)
- Monitor Coatings Limited (Project Partner)
- Micro Materials Ltd (Project Partner)
- GE Power (Project Partner)
- AWE plc (Project Partner)
- Manufacturing Technology Centre (United Kingdom) (Project Partner)
- M-Solv Ltd (Project Partner)
Publications
Goel S
(2020)
Horizons of modern molecular dynamics simulation in digitalized solid freeform fabrication with advanced materials
in Materials Today Chemistry
Khatri N
(2020)
Surface defects incorporated diamond machining of silicon
Khatri N
(2020)
Surface defects incorporated diamond machining of silicon
in International Journal of Extreme Manufacturing
Neha Khatri
(2020)
Surface defects incorporated diamond machining of silicon
Rai P
(2020)
Tackling COVID-19 pandemic through nanocoatings: Confront and exactitude
in Current Research in Green and Sustainable Chemistry
Ates B
(2020)
Chemistry, Structures, and Advanced Applications of Nanocomposites from Biorenewable Resources.
in Chemical reviews
Goel S
(2020)
Resilient and agile engineering solutions to address societal challenges such as coronavirus pandemic.
in Materials today. Chemistry
Fazeli Jadidi M
(2020)
Distribution of shallow NV centers in diamond revealed by photoluminescence spectroscopy and nanomachining
in Carbon
Neha Khatri
(2020)
Surface defects incorporated diamond machining of silicon
Oyekan J
(2020)
Applying a 6 DoF Robotic Arm and Digital Twin to Automate Fan-Blade Reconditioning for Aerospace Maintenance, Repair, and Overhaul.
in Sensors (Basel, Switzerland)
Khatri N
(2020)
Surface defects incorporated diamond machining of silicon
Huang N
(2020)
Elastic recovery of monocrystalline silicon during ultra-fine rotational grinding
in Precision Engineering
Khatri N
(2020)
Surface defects incorporated diamond machining of silicon
Rogov AB
(2020)
Relaxation Kinetics of Plasma Electrolytic Oxidation Coated Al Electrode: Insight into the Role of Negative Current.
in The journal of physical chemistry. C, Nanomaterials and interfaces
Labus Zlatanovic D
(2020)
An experimental study on lap joining of multiple sheets of aluminium alloy (AA 5754) using friction stir spot welding
in The International Journal of Advanced Manufacturing Technology
Khatri N
(2020)
Surface defects incorporated diamond machining of silicon
Viswanathan V
(2021)
Role of thermal spray in combating climate change
in Emergent Materials
Pan Y
(2021)
New insights into the methods for predicting ground surface roughness in the age of digitalisation
in Precision Engineering
Rogov A
(2021)
Toward rational design of ceramic coatings generated on valve metals by plasma electrolytic oxidation: The role of cathodic polarisation
in Ceramics International
Yin J
(2021)
An analytical model to predict the depth of sub-surface damage for grinding of brittle materials
in CIRP Journal of Manufacturing Science and Technology
Kumar Mishra R
(2021)
Computational prediction of electrical and thermal properties of graphene and BaTiO3 reinforced epoxy nanocomposites
in Biomaterials and Polymers Horizon
LarraƱaga-Altuna M
(2021)
Bactericidal surfaces: An emerging 21st-century ultra-precision manufacturing and materials puzzle
in Applied Physics Reviews
Fan P
(2021)
Origins of ductile plasticity in a polycrystalline gallium arsenide during scratching: MD simulation study
in Applied Surface Science
Goel G
(2021)
A bibliometric study on biomimetic and bioinspired membranes for water filtration
in npj Clean Water
NovƔk P
(2021)
Solutions of Critical Raw Materials Issues Regarding Iron-Based Alloys.
in Materials (Basel, Switzerland)
Mahapatra SD
(2021)
Piezoelectric Materials for Energy Harvesting and Sensing Applications: Roadmap for Future Smart Materials.
in Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Shishkin A
(2021)
Using circular economy principles to recycle materials in guiding the design of a wet scrubber-reactor for indoor air disinfection from coronavirus and other pathogens.
in Environmental technology & innovation
Katiyar N
(2021)
Nature-inspired materials: Emerging trends and prospects
in NPG Asia Materials
Fan P
(2021)
Molecular dynamics simulation of AFM tip-based hot scratching of nanocrystalline GaAs
in Materials Science in Semiconductor Processing
Fan P
(2021)
Atomic-Scale Friction Studies on Single-Crystal Gallium Arsenide Using Atomic Force Microscope and Molecular Dynamics Simulation
in Nanomanufacturing and Metrology
Labus Zlatanovic D
(2021)
Influence of Tool Geometry and Process Parameters on the Properties of Friction Stir Spot Welded Multiple (AA 5754 H111) Aluminium Sheets
in Materials
Wang Y
(2021)
Fabrication of three-dimensional sin-shaped ripples using a multi-tip diamond tool based on the force modulation approach
in Journal of Manufacturing Processes
Faisal N
(2021)
Large-scale manufacturing route to metamaterial coatings using thermal spray techniques and their response to solar radiation
in Emergent Materials
Popov V
(2021)
Novel hybrid method to additively manufacture denser graphite structures using Binder Jetting
in Scientific Reports
Guo Y
(2022)
Plasma electrolytic oxidation of magnesium by sawtooth pulse current
in Surface and Coatings Technology
Labus Zlatanovic D
(2022)
Influence of rotational speed on the electrical and mechanical properties of the friction stir spot welded aluminium alloy sheets
in Welding in the World
Sahu A
(2022)
A hybrid Grey-TOPSIS based quantum behaved particle swarm optimization for selection of electrode material to machine Ti6Al4V by electro-discharge machining
in Journal of the Brazilian Society of Mechanical Sciences and Engineering
Warsame C
(2022)
Modal analysis of novel coronavirus (SARS COV-2) using finite element methodology.
in Journal of the mechanical behavior of biomedical materials
Hawi S
(2022)
Critical Review of Nanopillar-Based Mechanobactericidal Systems
in ACS Applied Nano Materials
Forrest R
(2022)
Quantifying the differences in properties between polycrystals containing planar and curved grain boundaries
in Nanofabrication
Parris G
(2022)
A critical review of the developments in molecular dynamics simulations to study femtosecond laser ablation
in Materials Today: Proceedings
Popov V
(2022)
Author Correction: Novel hybrid method to additively manufacture denser graphite structures using Binder Jetting.
in Scientific reports
Fan P
(2022)
Uniaxial pulling and nano-scratching of a newly synthesized high entropy alloy
in APL Materials
Mir A
(2022)
Challenges and issues in continuum modelling of tribology, wear, cutting and other processes involving high-strain rate plastic deformation of metals.
in Journal of the mechanical behavior of biomedical materials
Mishra K
(2022)
Ionic Liquid-Based Polymer Nanocomposites for Sensors, Energy, Biomedicine, and Environmental Applications: Roadmap to the Future.
in Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Fan P
(2022)
Scanning Probe Lithography: State-of-the-Art and Future Perspectives.
in Micromachines
| Title | Nature inspired materials: Emerging trends and future prospects |
| Description | Nature inspired materials images |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2020 |
| URL | https://cord.cranfield.ac.uk/articles/figure/Nature_inspired_materials_Emerging_trends_and_future_pr... |
| Description | 1. Thermal spray monitoring systems developed. - LSBU 2. Digital Twins developed for plasma electrolytic processes. - The University of Manchester |
| Exploitation Route | Ongoing work |
| Sectors | Digital/Communication/Information Technologies (including Software) Energy Manufacturing including Industrial Biotechology Retail |
| Description | Important industry discussions have taken place, however, the conclusion is yet to be seen. Many discussions still ongoing. |
| First Year Of Impact | 2023 |
| Sector | Digital/Communication/Information Technologies (including Software),Manufacturing, including Industrial Biotechology |
| Impact Types | Economic |
| Title | Dataset: A Spectroscopic Reflectance-Based Low-Cost Thickness Measurement System for Thin Films: Development and Testing |
| Description | This data repository contains the following information related to the Article: "A Spectroscopic Reflectance-Based Low-Cost Thickness Measurement System for Thin Films: Development and Testing" File 1: RMSE and MSE calculation for SENSOR1 File 2: RMSE and MSE calculation for SENSOR2 File 3: RMSE and MSE calculation for SENSOR2 using HAL/DEUT light source File 4: Interference Interval Method Calculation and Reflectance Curve Modelling |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://figshare.shef.ac.uk/articles/dataset/_strong_Dataset_A_Spectroscopic_Reflectance-Based_Low-C... |
| Title | Data supporting 'Thermal response of multi-layer UV crosslinked PEGDA hydrogels' |
| Description | All data sets are raw data from thermoresponse behaviour of hydrogels. 1. Swelling test for multi-150 um hydrogels with 1.8 mg/ml of photoabsorber.2. Swelling test for mono-5 mm hydrogels with 0 mg/ml of photoabsorber.3. Swelling test for multi-20 um hydrogels with 9 mg/ml of photoabsorber.4. Swelling test for mono-3 mm and mono-1.5 mm hydrogels with 0 mg/ml of photoabsorber.5. Cyclic test for multi-150 um hydrogels.6. Dried weight and solid residue weight of all hydrogels samples7. EWC, NWF, NVF-summary for all hydrogel samples8. DSC-TG-Thermogram-All sample types |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://cord.cranfield.ac.uk/articles/dataset/Thermal_response_of_multi-layer_UV_crosslinked_PEGDA_h... |
| Title | Data supporting the publication 'Clay swelling: role of cations in stabilizing/destabilizing mechanisms' |
| Description | In the compressed dataset, there are two subdirectories, one in the name of 'Example' and another 'PostprocessData'. The Example directory contains input files, output data and postprocessed data for case Na12 starting at a d-space of onelayer value, where files starts with in.* are input files for lammps software, files ending with .dat or .lmptrj are output files from lammps, and files ending with .mat are matlab processed data. The 'postporcessedata' contains matlab processed results for all simulations in this study, contains simulation for NaMMT, KMMT, CaMMT and NaBD starting at onelayer, twolayer and threelayer d-space values. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://cord.cranfield.ac.uk/articles/dataset/Data_supporting_the_publication_Clay_swelling_role_of_... |
| Title | Data supporting the publication 'Clay swelling: role of cations in stabilizing/destabilizing mechanisms' |
| Description | In the compressed dataset, there are two subdirectories, one in the name of 'Example' and another 'PostprocessData'. The Example directory contains input files, output data and postprocessed data for case Na12 starting at a d-space of onelayer value, where files starts with in.* are input files for lammps software, files ending with .dat or .lmptrj are output files from lammps, and files ending with .mat are matlab processed data. The 'postporcessedata' contains matlab processed results for all simulations in this study, contains simulation for NaMMT, KMMT, CaMMT and NaBD starting at onelayer, twolayer and threelayer d-space values. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://cord.cranfield.ac.uk/articles/dataset/Data_supporting_the_publication_Clay_swelling_role_of_... |
| Title | Data supporting: 'Mechanical Behavior of 3D Printed Poly(ethylene glycol) Diacrylate Hydrogels in Hydrated Conditions Investigated Using Atomic Force Microscopy' |
| Description | 1. File AFM-Lines: Raw files for all force-distance curves along with excel file summarizing all the indentions on a single line taken at different height on the surface of the hydrogel. 2. File AFM-Maps: Raw files for all force-distance curves along with excel file summarizing all the indentation maps taken at the middle section on the surface of the hydrogel. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://cord.cranfield.ac.uk/articles/dataset/Data_supporting_Mechanical_Behavior_of_3D_Printed_Poly... |
| Title | Data supporting: 'Mechanical Behavior of 3D Printed Poly(ethylene glycol) Diacrylate Hydrogels in Hydrated Conditions Investigated Using Atomic Force Microscopy' |
| Description | 1. File AFM-Lines: Raw files for all force-distance curves along with excel file summarizing all the indentions on a single line taken at different height on the surface of the hydrogel. 2. File AFM-Maps: Raw files for all force-distance curves along with excel file summarizing all the indentation maps taken at the middle section on the surface of the hydrogel. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://cord.cranfield.ac.uk/articles/dataset/Data_supporting_Mechanical_Behavior_of_3D_Printed_Poly... |
| Title | Data supporting: 'Nanoindentation Response of 3D Printed PEGDA Hydrogels in a Hydrated Environment' |
| Description | Raw and processed data sets from nanoidentation response of 3D printed hydrogels. 1. Raw data of nanoindentation response. 2. Representative load-displacement curves for each type of hydrogel. 3. Representative data for creep for different types of hydrogels. 4. Representative data for NMR spectras of different types of hydrogels. 5. Representative data for glass transition of different types of hydrogels. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://cord.cranfield.ac.uk/articles/dataset/Nanoindentation_Response_of_3D_Printed_PEGDA_Hydrogels... |
| Title | Monitoring of Argon plasma in a coating manufacturing process by utilising IR imaging techniques Thermal Camera Recordings (CSQ Files) |
| Description | DescriptionThis dataset contains the thermal imaging recordings made during the experiments investigating the effect of gas flow rate on the shape of the plasma. The index at the end of the file name denotes the order they were taken. The plasma tool head is positioned on the right of the image with the plasma and powder travelling from the right to the left of the image.File FormatThe recordings are in FLIR's Compressed Sequence File format called CSQ. The recordings were made with the emissivity set to 0.94 as the true emissivity was not known at the time. It is recommended to get FLIRs tools to change the emissivity and observe the effects. CSQ files can be viewed with a free license for FLIR Thermal Studio but a paid license is needed to extract temperature values to make the NPZ files uploaded at https://doi.org/10.15131/shef.data.25335319. The metadata of the files (date, time, recording parameters etc.) can be extracted with a tool called exiftool; a custom class has been written as part of the project for inspecting these files and extracting the raw values. The specification is commonly used in thermal imaging.Process Parameterssheffield_doe_flowrate_gasrate_0001: 30V, 500A, 40 SL/MINsheffield_doe_flowrate_gasrate_0002: 30V, 500A, 40 SL/MINsheffield_doe_flowrate_gasrate_0003: 30V, 500A, 50 SL/MINsheffield_doe_flowrate_gasrate_0004: 30V, 500A, 60 SL/MINsheffield_doe_flowrate_gasrate_0005: 30V, 500A, 70 SL/MINsheffield_doe_flowrate_gasrate_0006: 30V, 500A, 80 SL/MINsheffield_doe_flowrate_gasrate_0007: 30V, 500A, 80 SL/MINsheffield_doe_flowrate_gasrate_0008: 30V, 500A, 80 SL/MINRecording Notessheffield_doe_flowrate_gasrate_0001: First run failed due to camera running out of batterysheffield_doe_flowrate_gasrate_0002: Good runsheffield_doe_flowrate_gasrate_0003: Starts with PULSE material flow rate rather than fixedsheffield_doe_flowrate_gasrate_0004: Powder level running low. May affect contrast and luminositysheffield_doe_flowrate_gasrate_0005: Stopped early due to loss of power to recording PCsheffield_doe_flowrate_gasrate_0006: Ran out of gassheffield_doe_flowrate_gasrate_0007: Redo of sheffield_doe_flowrate_gasrate_0006sheffield_doe_flowrate_gasrate_0008: Start of the recording had recording range set to 20 C - 120 C range and was corrected part way through. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://orda.shef.ac.uk/articles/dataset/Monitoring_of_Argon_plasma_in_a_coating_manufacturing_proce... |
| Title | Monitoring of Argon plasma in a coating manufacturing process by utilising IR imaging techniques Thermal Camera Recordings (CSQ Files) |
| Description | DescriptionThis dataset contains the thermal imaging recordings made during the experiments investigating the effect of gas flow rate on the shape of the plasma. The index at the end of the file name denotes the order they were taken. The plasma tool head is positioned on the right of the image with the plasma and powder travelling from the right to the left of the image.File FormatThe recordings are in FLIR's Compressed Sequence File format called CSQ. The recordings were made with the emissivity set to 0.94 as the true emissivity was not known at the time. It is recommended to get FLIRs tools to change the emissivity and observe the effects. CSQ files can be viewed with a free license for FLIR Thermal Studio but a paid license is needed to extract temperature values to make the NPZ files uploaded at https://doi.org/10.15131/shef.data.25335319. The metadata of the files (date, time, recording parameters etc.) can be extracted with a tool called exiftool; a custom class has been written as part of the project for inspecting these files and extracting the raw values. The specification is commonly used in thermal imaging.Process Parameterssheffield_doe_flowrate_gasrate_0001: 30V, 500A, 40 SL/MINsheffield_doe_flowrate_gasrate_0002: 30V, 500A, 40 SL/MINsheffield_doe_flowrate_gasrate_0003: 30V, 500A, 50 SL/MINsheffield_doe_flowrate_gasrate_0004: 30V, 500A, 60 SL/MINsheffield_doe_flowrate_gasrate_0005: 30V, 500A, 70 SL/MINsheffield_doe_flowrate_gasrate_0006: 30V, 500A, 80 SL/MINsheffield_doe_flowrate_gasrate_0007: 30V, 500A, 80 SL/MINsheffield_doe_flowrate_gasrate_0008: 30V, 500A, 80 SL/MINRecording Notessheffield_doe_flowrate_gasrate_0001: First run failed due to camera running out of batterysheffield_doe_flowrate_gasrate_0002: Good runsheffield_doe_flowrate_gasrate_0003: Starts with PULSE material flow rate rather than fixedsheffield_doe_flowrate_gasrate_0004: Powder level running low. May affect contrast and luminositysheffield_doe_flowrate_gasrate_0005: Stopped early due to loss of power to recording PCsheffield_doe_flowrate_gasrate_0006: Ran out of gassheffield_doe_flowrate_gasrate_0007: Redo of sheffield_doe_flowrate_gasrate_0006sheffield_doe_flowrate_gasrate_0008: Start of the recording had recording range set to 20 C - 120 C range and was corrected part way through. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://orda.shef.ac.uk/articles/dataset/Monitoring_of_Argon_plasma_in_a_coating_manufacturing_proce... |
| Title | Monitoring of Argon plasma in a coating manufacturing process by utilising IR imaging techniques Thermal Recordings (NPZ Files) |
| Description | DescriptionThis dataset contains the thermal imaging recordings made during the experiments investigating the effect of gas flow rate on the shape of the plasma. The index at the end of the file name denotes the order they were taken. The plasma tool head is positioned on the right of the image with the plasma and powder travelling from the right to the left of the image.These files are the temperature exported at an emissivity of 0.74, the emissivity of the tool head. These are the corresponding files for the recordings in the collection Thermal Camera Recordings (CSQ Files).File FormatThe files are in NPZ format and can be loaded into Python using the np.load method. The array shape NxHxW where N is the number of frames, H is height and W is width.Process Parameterssheffield_doe_flowrate_gasrate_0001: 30V, 500A, 40 SL/MINsheffield_doe_flowrate_gasrate_0002: 30V, 500A, 40 SL/MINsheffield_doe_flowrate_gasrate_0003: 30V, 500A, 50 SL/MINsheffield_doe_flowrate_gasrate_0004: 30V, 500A, 60 SL/MINsheffield_doe_flowrate_gasrate_0005: 30V, 500A, 70 SL/MINsheffield_doe_flowrate_gasrate_0006: 30V, 500A, 80 SL/MINsheffield_doe_flowrate_gasrate_0007: 30V, 500A, 80 SL/MINsheffield_doe_flowrate_gasrate_0008: 30V, 500A, 80 SL/MINRecording Notessheffield_doe_flowrate_gasrate_0001: First run failed due to camera running out of batterysheffield_doe_flowrate_gasrate_0002: Good runsheffield_doe_flowrate_gasrate_0003: Starts with PULSE material flow rate rather than fixedsheffield_doe_flowrate_gasrate_0004: Powder level running low. May affect contrast and luminositysheffield_doe_flowrate_gasrate_0005: Stopped early due to loss of power to recording PCsheffield_doe_flowrate_gasrate_0006: Ran out of gassheffield_doe_flowrate_gasrate_0007: Redo of sheffield_doe_flowrate_gasrate_0006sheffield_doe_flowrate_gasrate_0008: Start of the recording had recording range set to 20 C - 120 C range and was corrected part way through. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://orda.shef.ac.uk/articles/dataset/Monitoring_of_Argon_plasma_in_a_coating_manufacturing_proce... |
| Title | Monitoring of Argon plasma in a coating manufacturing process by utilising IR imaging techniques Thermal Recordings (NPZ Files) |
| Description | DescriptionThis dataset contains the thermal imaging recordings made during the experiments investigating the effect of gas flow rate on the shape of the plasma. The index at the end of the file name denotes the order they were taken. The plasma tool head is positioned on the right of the image with the plasma and powder travelling from the right to the left of the image.These files are the temperature exported at an emissivity of 0.74, the emissivity of the tool head. These are the corresponding files for the recordings in the collection Thermal Camera Recordings (CSQ Files).File FormatThe files are in NPZ format and can be loaded into Python using the np.load method. The array shape NxHxW where N is the number of frames, H is height and W is width.Process Parameterssheffield_doe_flowrate_gasrate_0001: 30V, 500A, 40 SL/MINsheffield_doe_flowrate_gasrate_0002: 30V, 500A, 40 SL/MINsheffield_doe_flowrate_gasrate_0003: 30V, 500A, 50 SL/MINsheffield_doe_flowrate_gasrate_0004: 30V, 500A, 60 SL/MINsheffield_doe_flowrate_gasrate_0005: 30V, 500A, 70 SL/MINsheffield_doe_flowrate_gasrate_0006: 30V, 500A, 80 SL/MINsheffield_doe_flowrate_gasrate_0007: 30V, 500A, 80 SL/MINsheffield_doe_flowrate_gasrate_0008: 30V, 500A, 80 SL/MINRecording Notessheffield_doe_flowrate_gasrate_0001: First run failed due to camera running out of batterysheffield_doe_flowrate_gasrate_0002: Good runsheffield_doe_flowrate_gasrate_0003: Starts with PULSE material flow rate rather than fixedsheffield_doe_flowrate_gasrate_0004: Powder level running low. May affect contrast and luminositysheffield_doe_flowrate_gasrate_0005: Stopped early due to loss of power to recording PCsheffield_doe_flowrate_gasrate_0006: Ran out of gassheffield_doe_flowrate_gasrate_0007: Redo of sheffield_doe_flowrate_gasrate_0006sheffield_doe_flowrate_gasrate_0008: Start of the recording had recording range set to 20 C - 120 C range and was corrected part way through. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://orda.shef.ac.uk/articles/dataset/Monitoring_of_Argon_plasma_in_a_coating_manufacturing_proce... |
| Title | Nanoindentation Response of 3D Printed PEGDA Hydrogels in a Hydrated Environment |
| Description | Raw and processed data sets from nanoidentation response of 3D printed hydrogels. 1. Raw data of nanoindentation response. 2. Representative load-displacement curves for each type of hydrogel. 3. Representative data for creep for different types of hydrogels. 4. Representative data for NMR spectras of different types of hydrogels. 5. Representative data for glass transition of different types of hydrogels. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://cord.cranfield.ac.uk/articles/dataset/Nanoindentation_Response_of_3D_Printed_PEGDA_Hydrogels... |
| Title | Thermal response of multi-layer UV crosslinked PEGDA hydrogels |
| Description | All data sets are raw data from thermoresponse behaviour of hydrogels. 1. Swelling test for multi-150 um hydrogels with 1.8 mg/ml of photoabsorber.2. Swelling test for mono-5 mm hydrogels with 0 mg/ml of photoabsorber.3. Swelling test for multi-20 um hydrogels with 9 mg/ml of photoabsorber.4. Swelling test for mono-3 mm and mono-1.5 mm hydrogels with 0 mg/ml of photoabsorber.5. Cyclic test for multi-150 um hydrogels.6. Dried weight and solid residue weight of all hydrogels samples7. EWC, NWF, NVF-summary for all hydrogel samples8. DSC-TG-Thermogram-All sample types |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://cord.cranfield.ac.uk/articles/dataset/Thermal_response_of_multi-layer_UV_crosslinked_PEGDA_h... |
| Description | University of Twente (Netherlands, Europe) - joint PhD |
| Organisation | University of Twente |
| Country | Netherlands |
| Sector | Academic/University |
| PI Contribution | Joint supervision, research discussion and publication. Visit to student at partner institution. |
| Collaborator Contribution | PhD student based at the partner university |
| Impact | Joint Publications- https://www.sciencedirect.com/science/article/pii/S0257897224012283?via%3Dihub |
| Start Year | 2024 |