Yr Athro Cinzia Giannetti

Athro

Mechanical Engineering

c.giannetti@abertawe.ac.uk

305
Trydydd Llawr
Adeilad Gogleddol Peirianneg
Campws y Bae

Dolenni Ymchwil

https://orcid.org/0000-0003-0339-5872

ResearchGate

Google Scholar

Scopus

Trosolwg

Mae Dr Cinzia Gianetti yn Athro Cysylltiol mewn Peirianneg Fecanyddol. Mae ymchwil Cinzia wedi'i llywio gan frwdfrydedd dros ddatblygu technolegau a dulliau arloesol a all greu effaith drawsnewidiol ar ein bywydau, y gymdeithas a'r economi. Mae hi wedi bod yn aelod gweithgar o'r gymuned ymchwil Gweithgynhyrchu Digidol ac yn eiriolydd ers 2010, pan symudodd o fyd diwydiant i'r byd academaidd.

Cenhadaeth Cinzia yw cefnogi twf sector gweithgynhyrchu'r Deyrnas Unedig drwy ddatblygu systemau cynhyrchu annibynnol, cydweithredol a deallus drwy ddefnyddio technolegau digidol uwch sy'n ddwys o ran gwybodaeth. Mae Cinzia wedi derbyn Cymrodoriaeth Gweithgynhyrchu Digidol yr EPSRC UKRI (EP/S001387/1 2018-2021) ac mae hi'n Ddirprwy Gyfarwyddwr y Ganolfan Ymchwil 'Materials Made Smarter' (EP/V061798/1), lle mae hi'n arwain mentrau digitaleiddio, gan arloesi gwaith datblygu ac integreiddio deallusrwydd artiffisial mewn diwydiannau gweithgynhyrchu. Mae Cinzia yn gyd-Ymchwilydd ac yn aelod o'r tîm arweinyddiaeth amlddisgyblaethol yng Nghanolfan Hyfforddiant Doethurol yr EPSRC mewn "Gwella Rhyngweithiadau a Chydweithrediadau Dynol â Data a Systemau wedi'u llywio gan Ddeallusrwydd", sy'n hyfforddi ac yn meithrin arweinwyr y dyfodol mewn arloesiadau wedi'u llywio gan ddata.

Mae gan Cinzia brofiad helaeth o gyflawni arloesi ac effaith drwy brosiectau ymchwil a datblygu diwydiannol cymhwysol, profiad a enillwyd o fyd diwydiant a'r byd academaidd. Bu ganddi swyddi mewn rolau peiriannu uwch i gwmnïau byd-eang (Siemens/Motorola/NextGen) ac mae ganddi hanes llwyddiannus wrth gyflawni prosiectau Innovate UK.

Meysydd Arbenigedd

Peirianneg Systemau Gweithgynhyrchu
Data Mawr
Gwybodeg Gweithgynhyrchu ac IoTs
Dysgu Peirianyddol/Dysgu Dwfn
Deallusrwydd Artiffisial
Optimeiddio Prosesau

Uchafbwyntiau Gyrfa

Ymchwil

Mae ymchwil Dr Giannetti yn cyd-fynd yn agos â'r thema ymchwil Gweithgynhyrchu'r Dyfodol a maes blaenoriaeth yr EPSRC 'Gweithgynhyrchu Digidol'. Nod ei hymchwil yw cefnogi twf sector gweithgynhyrchu'r Deyrnas Unedig drwy ddatblygu dulliau deallusrwydd artiffisial i wella effeithlonrwydd a chynaliadwyedd prosesau gweithgynhyrchu.

Mae Dr Giannetti wedi cyfrannu'n sylweddol at ddatblygu ymagweddau newydd wedi'u llywio gan ddata at leihau aneffeithlonrwydd presennol mewn prosesau gweithgynhyrchu sy'n effeithio ar gostau gweithgynhyrchu, amser segur heb ei gynllunio, ansawdd a chynnyrch. Mae'r datblygiadau hyn wedi'u cymhwyso'n llwyddiannus i feysydd gwahanol ym myd diwydiant a gweithgynhyrchu, gan gynnwys: (i) lleihau diffygion mewn prosesau creu dur ac optimeiddio perfformiad tanwydd mewn gweithrediadau ffwrneisi chwyddo (y diwydiant dur); (ii) canfod ar gyfer dadansoddi awtomataidd aneffeithlonrwydd gweithgynhyrchu wrth greu caniau (y diwydiant pecynnu); (iii) optimeiddio prosesau cydosod roboteg.

Canlyniad pwysig ymchwil Dr Giannetti yw gwella ein dealltwriaeth o fanteision mabwysiadu Technolegau Digidol Diwydiannol, a'u gwerth, a magu hyder gweithgynhyrchwyr a chyflenwyr i fuddsoddi yn y maes hwn, gan gyfrannu at y Deyrnas Unedig yn dod yn arweinydd mewn gweithgynhyrchu digidol.

Cydweithrediadau

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Flynn, J., Giannetti, C., & Dijk, H. (2023). Anomaly Detection of DC Nut Runner Processes in Engine Assembly. AI, 4(1), 234-254.
https://doi.org/10.3390/ai4010010, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62914
http://dx.doi.org/10.3390/ai4010010
Griffiths, C., Giannetti, C., Andrzejewski, K., & Morgan, A. (2021). Comparison of a bat and genetic algorithm generated sequence against lead through programming when assembling a PCB using a 6 axis robot with multiple motions and speeds. IEEE Transactions on Industrial Informatics, 18(2), 1-1.
https://doi.org/10.1109/tii.2021.3082877, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56974
Borghini, E., Giannetti, C., Flynn, J., & Todeschini, G. (2021). Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation. Energies, 14(12), 3453
https://doi.org/10.3390/en14123453, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57063
Giannetti, C. & Essien, A. (2022). Towards scalable and reusable predictive models for cyber twins in manufacturing systems. Journal of Intelligent Manufacturing, 33(2), 441-455.
https://doi.org/10.1007/s10845-021-01804-0, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57612
Borghini, E. & Giannetti, C. (2021). Short Term Load Forecasting Using TabNet: A Comparative Study with Traditional State-of-the-Art Regression Models. Engineering Proceedings, 5(1), 6
https://doi.org/10.3390/engproc2021005006, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57695
Cao, Q., Zanni-Merk, C., Samet, A., Reich, C., Beuvron, F., Beckmann, A., & Giannetti, C. (2022). KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0. Robotics and Computer-Integrated Manufacturing, 74, 102281
https://doi.org/10.1016/j.rcim.2021.102281, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58508
Todeschini, G., Kheta, K., & Giannetti, C. (2022). An image-based deep transfer learning approach to classify power quality disturbances. Electric Power Systems Research, 213, 108795
https://doi.org/10.1016/j.epsr.2022.108795, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61303
Lakshmanan, K., Borghini, E., Beckmann, A., Pleydell-Pearce, C., & Giannetti, C. (2022). Data modelling and Remaining Useful Life estimation of rolls in a steel making cold rolling process. Procedia Computer Science, 207(C), 1057-1066.
https://doi.org/10.1016/j.procs.2022.09.161, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61602
O'DONOVAN, C., POPOV, I., Todeschini, G., & Giannetti, C. (2023). Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation. The International Journal of Advanced Manufacturing Technology, 126(3-4), 1397-1416.
https://doi.org/10.1007/s00170-023-11151-4, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62838
http://dx.doi.org/10.1007/s00170-023-11151-4
Ransing, R., Giannetti, C., Ransing, M., James, M., Ransing, R., & Giannetti, C. (2013). A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects. Computers in Industry, 64(5), 514-523.
https://doi.org/10.1016/j.compind.2013.02.009, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa14574

Holl Ymchwil

Erthyglau Cylchgronau

O'DONOVAN, C., Giannetti, C., & Pleydell-Pearce, C. (2024). Real-time monitoring of molten zinc splatter using machine learning-based computer vision. Journal of Intelligent Manufacturing, 0
https://doi.org/10.1007/s10845-024-02418-y, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa66472
Giannetti, C., Borghini, E., Carr, A., Raleigh, J., & Rackham, B. (2024). Deep learning for robust forecasting of hot metal silicon content in a blast furnace. The International Journal of Advanced Manufacturing Technology, 0
https://doi.org/10.1007/s00170-024-13214-6, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65825
Beden, S., Lakshmanan, K., Giannetti, C., & Beckmann, A. (2023). Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning. Applied Sciences, 13(23), 12778
https://doi.org/10.3390/app132312778, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65211
Latham, S. & Giannetti, C. (2023). A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill. SN Computer Science, 4(5)
https://doi.org/10.1007/s42979-023-02104-5, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64007
http://dx.doi.org/10.1007/s42979-023-02104-5
Lakshmanan, K., Roach, M., Giannetti, C., Bhoite, S., George, D., Mortensen, T., Manduhu, M., Heravi, B., Kariyawasam, S., & Xie, X. (2023). A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods. AI, 4(2), 461-481.
https://doi.org/10.3390/ai4020025, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63515
O'DONOVAN, C., POPOV, I., Todeschini, G., & Giannetti, C. (2023). Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation. The International Journal of Advanced Manufacturing Technology, 126(3-4), 1397-1416.
https://doi.org/10.1007/s00170-023-11151-4, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62838
http://dx.doi.org/10.1007/s00170-023-11151-4
Flynn, J., Giannetti, C., & Dijk, H. (2023). Anomaly Detection of DC Nut Runner Processes in Engine Assembly. AI, 4(1), 234-254.
https://doi.org/10.3390/ai4010010, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62914
http://dx.doi.org/10.3390/ai4010010
Todeschini, G., Kheta, K., & Giannetti, C. (2022). An image-based deep transfer learning approach to classify power quality disturbances. Electric Power Systems Research, 213, 108795
https://doi.org/10.1016/j.epsr.2022.108795, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61303
Lakshmanan, K., Borghini, E., Beckmann, A., Pleydell-Pearce, C., & Giannetti, C. (2022). Data modelling and Remaining Useful Life estimation of rolls in a steel making cold rolling process. Procedia Computer Science, 207(C), 1057-1066.
https://doi.org/10.1016/j.procs.2022.09.161, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61602
Cao, Q., Zanni-Merk, C., Samet, A., Reich, C., Beuvron, F., Beckmann, A., & Giannetti, C. (2022). KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0. Robotics and Computer-Integrated Manufacturing, 74, 102281
https://doi.org/10.1016/j.rcim.2021.102281, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa58508
Giannetti, C. & Essien, A. (2022). Towards scalable and reusable predictive models for cyber twins in manufacturing systems. Journal of Intelligent Manufacturing, 33(2), 441-455.
https://doi.org/10.1007/s10845-021-01804-0, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57612
Borghini, E. & Giannetti, C. (2021). Short Term Load Forecasting Using TabNet: A Comparative Study with Traditional State-of-the-Art Regression Models. Engineering Proceedings, 5(1), 6
https://doi.org/10.3390/engproc2021005006, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57695
Borghini, E., Giannetti, C., Flynn, J., & Todeschini, G. (2021). Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation. Energies, 14(12), 3453
https://doi.org/10.3390/en14123453, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa57063
Griffiths, C., Giannetti, C., Andrzejewski, K., & Morgan, A. (2021). Comparison of a bat and genetic algorithm generated sequence against lead through programming when assembling a PCB using a 6 axis robot with multiple motions and speeds. IEEE Transactions on Industrial Informatics, 18(2), 1-1.
https://doi.org/10.1109/tii.2021.3082877, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56974
Flynn, J. & Giannetti, C. (2021). Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging. AI, 2(1), 135-149.
https://doi.org/10.3390/ai2010009, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56687
Aldoumani, N., Giannetti, C., Abdallah, Z., Belblidia, F., Haddad Khodaparast, H., Friswell, M., & Sienz, J. (2020). Optimisation of the Filament Winding Approach Using a Newly Developed In-House Uncertainty Model. Eng, 1(2), 122-136.
https://doi.org/10.3390/eng1020008, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa56686
Essien, A. & Giannetti, C. (2020). A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders. IEEE Transactions on Industrial Informatics, 16(9), 6069-6078.
https://doi.org/10.1109/tii.2020.2967556, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa53318
Cooper, M., Griffiths, C., Andrzejewski, K., & Giannetti, C. (2019). Motion optimisation for improved cycle time and reduced vibration in robotic assembly of electronic components. AIMS Electronics and Electrical Engineering, 3(3), 274-289.
https://doi.org/10.3934/electreng.2019.3.274, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa51716
Giannetti, C., Lucini, B., & Vadacchino, D. (2019). Machine Learning as a universal tool for quantitative investigations of phase transitions. Nuclear Physics B, 944, 114639
https://doi.org/10.1016/j.nuclphysb.2019.114639, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa50366
Aldoumani, N., Haddad Khodaparast, H., Giannetti, C., Abdallah, Z., Cameron, I., Friswell, M., & Sienz, J. (2018). A robust design of an innovative shaped rebar system using a novel uncertainty model. Structural and Multidisciplinary Optimization, 58(4), 1351-1365.
https://doi.org/10.1007/s00158-018-2050-z, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa40978
Andrzejewski, K., Cooper, M., Griffiths, C., Giannetti, C., Giannetti, C., & Griffiths, C. (2018). Optimisation process for robotic assembly of electronic components. The International Journal of Advanced Manufacturing Technology
https://doi.org/10.1007/s00170-018-2645-y, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa43763
Giannetti, C. & Ransing, R. (2016). Risk based uncertainty quantification to improve robustness of manufacturing operations. Computers & Industrial Engineering, 101, 70-80.
https://doi.org/10.1016/j.cie.2016.08.002, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa29528
Ransing, R., Batbooti, R., & Giannetti, C. (2016). A quality correlation algorithm for tolerance synthesis in manufacturing operations. Computers & Industrial Engineering, 93, 1-11.
https://doi.org/10.1016/j.cie.2015.12.008, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa25053
Giannetti, C., Ransing, M., Ransing, R., Bould, D., Gethin, D., & Sienz, J. (2015). Organisational Knowledge Management for Defect Reduction and Sustainable Development in Foundries. International Journal of Knowledge and Systems Science, 6(3), 18-37.
https://doi.org/10.4018/ijkss.2015070102, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa21931
Giannetti, C., Ransing, R., Ransing, M., Bould, D., Gethin, D., & Sienz, J. (2014). A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data. Computers & Industrial Engineering, 72, 217-229.
https://doi.org/10.1016/j.cie.2014.03.017, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa21495
Ransing, R., Giannetti, C., Ransing, M., James, M., Ransing, R., & Giannetti, C. (2013). A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects. Computers in Industry, 64(5), 514-523.
https://doi.org/10.1016/j.compind.2013.02.009, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa14574

Cyfraniadau Cynadleddau

Essien, A. & Giannetti, C. (2019). A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders. In 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-6). IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA).
https://doi.org/10.1109/INISTA.2019.8778417, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa51622
Giannetti, C. (2017). A framework for improving process robustness with quantification of uncertainties in Industry 4.0. In INnovations in Intelligent SysTems and Applications (INISTA), 2017 IEEE International Conference on (pp. 189-194).
https://doi.org/10.1109/INISTA.2017.8001155, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa38821
Giannetti, C. (2014). Product Specific Process Knowledge Discovery Using Co-Linearity Index and Penalty Functions to Support Process FMEA in the Steel Industry. In CIE44 & IMSS'14 ProceedingsCIE44 & IMSS'14.
SU Repository: https://cronfa.swan.ac.uk/Record/cronfa43764

Goruchwyliaeth

Untitled (cyfredol)

PhD

Goruchwyliwr arall: Prof Matthew Davies
Untitled (cyfredol)

PhD

Goruchwyliwr arall: Dr Alma Rahat

Goruchwyliwr arall: Prof Simon Robinson

Goruchwyliwr arall: Prof Matt Jones
Hybrid AI for Supply Chain Optimization in Industry 4.0 (cyfredol)

PhD

Goruchwyliwr arall: Prof Arnold Beckmann
Leveraging machine learning techniques for understanding, monitoring, and optimisation of the cooling process on the Run-out table at the Hot-Strip Mill, Tata Steel, Port Talbot.«br /»«br /»«br /»«br /»«br /»«br /» (cyfredol)

PhD

Goruchwyliwr arall: Prof Cameron Pleydell-Pearce
Forecasting nearshore waves using Artificial Neural Network (cyfredol)

PhD

Goruchwyliwr arall: Prof Harshinie Karunarathna
Development and Implementation of Intuitive Human-Machine Interaction System. (cyfredol)

PhD

Goruchwyliwr arall: Dr Chunxu Li
Developing Novel Machine Learning Techniques with Human-in-the-Loop Approach to Enable Better Decision Making on Operations Maintenance (cyfredol)

PhD

Goruchwyliwr arall: Dr Muneeb Ahmad
Application of Artificial Intelligence for Industrial Remote Condition Monitoring Challenges (cyfredol)

EngD

Goruchwyliwr arall: Prof Cameron Pleydell-Pearce
Understanding Scale Growth on Carbon Steel Tubes during High Temperature Processing (cyfredol)

EngD

Goruchwyliwr arall: Dr Elizabeth Sackett
The Sustainable Adoption of Industry 4.0 & Machine Learning in the Automotive Industry (dyfarnwyd 2023)

EngD

Goruchwyliwr arall: Dr Christian Griffiths
Development and integration of Industry 4.0 Collaborative Robots into Engine Manufacture (cyfredol)

EngD

Goruchwyliwr arall: Dr Christian Griffiths
Enhancing Decision-Making for Root Cause Analysis of Width-Related Defects in the Hot Strip Mill using Machine Learning (cyfredol)

EngD

Goruchwyliwr arall: Prof Cameron Pleydell-Pearce

Modiwlau a Addysgir

EG-M07 Optimisation

This module provides an introduction to some important techniques of optimisation that may be used across a broad range of engineering disciplines. The focus is on understanding the methods through hand calculation rather than the use of particular software packages. Numerical examples are employed to illustrate concepts and potential applications.
EGH310 Digital Manufacturing

Industrial production is entering a new and exciting era, called Smart Manufacturing that will bring a complete transformation in the way products are designed, manufactured and serviced through pervasive and ubiquitous use of ICT, sensors and intelligent robots. To fully exploit the potential of these new technologies there is the need to develop awareness of what Smart Manufacturing is and how organisations can embrace the necessary changes to drive efficiency and boost productivity. This module covers fundamental concepts, technologies and business strategies of Smart Manufacturing to equip the participants with the necessary interdisciplinary skills to become leaders and innovators in the design, deployment and operation of smart production systems or Factories of the Future.
EGSM24 Smart Manufacturing

Industrial production is entering a new and exciting era, called Smart Manufacturing that will bring a complete transformation in the way products are designed, manufactured and serviced through pervasive and ubiquitous use of ICT, sensors and intelligent robots. To fully exploit the potential of these new technologies there is the need to develop awareness of what Smart Manufacturing is and how organisations can embrace the necessary changes to drive efficiency and boost productivity. This module covers fundamental concepts, technologies and business strategies of Smart Manufacturing to equip the participants with the necessary interdisciplinary skills to become leaders and innovators in the design, deployment and operation of smart production systems or Factories of the Future.

Grantiau a Phrosiectau Allweddol

Transfer Learning for Robust, Resilient and Transferable Cyber Manufacturing Systems 2018 - 2021

, £477,185
EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems 2019 - 2027

, £4,986,846
PARSER: Parking and AiR pollution SEnsoRs for Smart Cities 2019 - 2021

, £714,254

Yr Athro Cinzia Giannetti

Cyfeiriad ebost

Dolenni Ymchwil

Trosolwg

Meysydd Arbenigedd

Uchafbwyntiau Gyrfa

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Holl Ymchwil

Erthyglau Cylchgronau

Cyfraniadau Cynadleddau

Goruchwyliaeth

Untitled (cyfredol)

Untitled (cyfredol)

Hybrid AI for Supply Chain Optimization in Industry 4.0 (cyfredol)

Leveraging machine learning techniques for understanding, monitoring, and optimisation of the cooling process on the Run-out table at the Hot-Strip Mill, Tata Steel, Port Talbot.«br /»«br /»«br /»«br /»«br /»«br /» (cyfredol)

Forecasting nearshore waves using Artificial Neural Network (cyfredol)

Development and Implementation of Intuitive Human-Machine Interaction System. (cyfredol)

Developing Novel Machine Learning Techniques with Human-in-the-Loop Approach to Enable Better Decision Making on Operations Maintenance (cyfredol)

Application of Artificial Intelligence for Industrial Remote Condition Monitoring Challenges (cyfredol)

Understanding Scale Growth on Carbon Steel Tubes during High Temperature Processing (cyfredol)

The Sustainable Adoption of Industry 4.0 & Machine Learning in the Automotive Industry (dyfarnwyd 2023)

Development and integration of Industry 4.0 Collaborative Robots into Engine Manufacture (cyfredol)

Enhancing Decision-Making for Root Cause Analysis of Width-Related Defects in the Hot Strip Mill using Machine Learning (cyfredol)

Modiwlau a Addysgir

EG-M07 Optimisation

EGH310 Digital Manufacturing

EGSM24 Smart Manufacturing

Grantiau a Phrosiectau Allweddol

Transfer Learning for Robust, Resilient and Transferable Cyber Manufacturing Systems 2018 - 2021

EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems 2019 - 2027

PARSER: Parking and AiR pollution SEnsoRs for Smart Cities 2019 - 2021

Yr Athro Cinzia Giannetti: Darlith Agoriadol, 2022