Professor Cinzia Giannetti

Professor, Mechanical Engineering

JavaScript is required to view this email address.

Office - 305
Third Floor
Engineering North
Bay Campus

Available For Postgraduate Supervision

Research Links

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

ResearchGate

Google Scholar

ResearcherID

Scopus

About

Dr Cinzia Giannetti is Associate Professor in Mechanical Engineering. Cinzia’s research is driven by a passion for developing innovative technologies and tools that can have a transformational impact on our lives, society and the economy. She has been an active member of the Digital Manufacturing research community and advocate since 2010, when she transitioned from industry to academia.

Cinzia's mission is to support the growth of the UK Manufacturing sector through development of autonomous, collaborative and intelligent production systems by using knowledge-intensive advanced digital technologies. Cinzia has been recipient of the UKRI EPSRC Digital Manufacturing Fellowship (EP/S001387/1 2018-2021) and she is Deputy Director of the Materials Made Smarter Research Centre (EP/V061798/1), where she leads digitalisation initiatives, pioneering the development and integration of AI in manufacturing industries. Cinzia is co-Investigator and part of the multidisciplinary leadership team in the EPSRC Centre for Doctoral Training in “Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems”, which train and nurture future leaders in digital data-driven innovations.

Cinzia has significant experience in delivering innovation and impact through applied industrial R&D projects gained both in industry and academia. She held positions in senior engineering roles for global companies (Siemens/Motorola/NextGen) and she has a successful track record in the delivery of Innovate UK projects.

Areas Of Expertise

Manufacturing Systems Engineering
Big Data
Manufacturing Informatics and IoTs
Machine Learning/Deep Learning
Artificial Intelligence
Process Optimisation

Career Highlights

Teaching Interests

Fellow of Higher Education Academy

Development and teaching of Smart Manufacturing

Research Collaborations

Publications

Research Highlights

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

All Research

Journal Articles

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
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
Lakshmanan, K., Roach, M., Giannetti, C., Bhoite, S., George, D., Mortensen, T., Manduhu, M., Heravi, B., Kariyawasam, S., & Xie, X. (n.d.) 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
http://dx.doi.org/10.3390/ai4020025
Giannetti, C., Borghini, E., Carr, A., Raleigh, J., & Rackham, B. (n.d.) Deep learning for robust forecasting of hot metal silicon content in a blast furnace. The International Journal of Advanced Manufacturing Technology
https://doi.org/10.1007/s00170-024-13214-6, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65825
http://dx.doi.org/10.1007/s00170-024-13214-6

Conference Contributions

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

Supervision

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

PhD

Other supervisor: 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 /» (current)

PhD

Other supervisor: Prof Cameron Pleydell-Pearce
Forecasting nearshore waves using Artificial Neural Network (current)

PhD

Other supervisor: Prof Harshinie Karunarathna
Development and Implementation of Intuitive Human-Machine Interaction System. (current)

PhD

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

PhD

Other supervisor: Dr Muneeb Ahmad
Application of Artificial Intelligence for Industrial Remote Condition Monitoring Challenges (current)

EngD

Other supervisor: Prof Cameron Pleydell-Pearce
The Sustainable Adoption of Industry 4.0 & Machine Learning in the Automotive Industry (awarded 2023)

EngD

Other supervisor: Dr Christian Griffiths
Development and integration of Industry 4.0 Collaborative Robots into Engine Manufacture (current)

EngD

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

EngD

Other supervisor: Prof Cameron Pleydell-Pearce

Taught Modules

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.

Key Grants and Projects

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
Made Smarter Innovation - Materials Made Smarter Research Centre 2021 - 2025

, with Professor I Todd (PI), £4,049,204

Professor Cinzia Giannetti

Email address

Research Links

About

Areas Of Expertise

Career Highlights

Research Highlights

All Research

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

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 /» (current)

Forecasting nearshore waves using Artificial Neural Network (current)

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

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

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

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

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

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

EG-M07 Optimisation

EGH310 Digital Manufacturing

EGSM24 Smart Manufacturing

Professor Cinzia Giannetti: Inaugural Lecture, 2022