Marco Giglio

Marco Giglio is Full Professor of Machine Design at the Department of Mechanical Engineering of Politecnico di Milano, where he graduated cum laude in 1988, and the scientific coordinator and team leader of SIGMA Lab. His research addresses the structural integrity of mechanical and aeronautical components and systems, from structural health monitoring and prognosis of helicopters and UAVs to ballistic impact, fatigue and crack growth. He has coordinated five European Defence Agency projects, from HECTOR in 2009 to BATTAGE, started in 2025, and has been a Governmental Expert in the EDA Captech Aerial Systems and Materials & Structures since 2018. He has headed the department’s Machine Design and Vehicles Section since 2013, and was Deputy Head of the department in 2018–2019. He has published more than 260 papers indexed in Scopus and was included in the October 2023 World Top 2% Scientists ranking.

• Structural integrity of mechanical and aeronautical structures through Structural Health Monitoring, especially helicopters and UAVs
• Health and usage monitoring systems (HUMS) and digital twins for defence platforms
• Low- and high-velocity (ballistic) impact damage and ballistic protections
• Fatigue life and crack growth prediction of helicopter components
• Fatigue design with defects
• Failure analysis and reconstruction of in-service failures
• Innovative solutions in the energy sector: concentrated solar power and microdrilling
• Diagnostics and prognostics of Li-ion batteries and hydrogen containment systems

1. Structural damage diagnosis and prognosis with fleet digital twin considering similarity of individual structural features

   J. Xu, D. Dai, X. Zhou, M. Giglio, C. Sbarufatti, L. Dong

   Aerospace Science and Technology, 168, 110983 · 2026

2. Reference-free distributed monitoring of deflections in multi-span bridges

   D. Poloni, M. Morgese, C. Wang, T. Taylor, M. Giglio, F. Ansari, C. Sbarufatti

   Engineering Structures, 323, 119277 · 2025

3. Variational Neural Network Embedded with Digital Twins for Probabilistic Structural Damage Quantification

   J. Xu, X. Zhou, M. Giglio, C. Sbarufatti, L. Dong

   AIAA Journal, 63(6), 2474-2486 · 2025

4. Anomaly characterization for the condition monitoring of rotating shafts exploiting data fusion and explainable convolutional neural networks

   M. Parziale, Y. Yeung, K. Youcef-Toumi, M. Giglio, F. Cadini

   Structural Health Monitoring, 25(3), 1678-1698 · 2025

5. In-service Load Monitoring for an UAV Digital Twin

   X. Zhou, M. Dziendzikowski, K. Dragan, L. Dong, M. Giglio, C. Sbarufatti

   e-Journal of Nondestructive Testing, 29(7) · 2024

6. Preliminary Nose Landing Gear Digital Twin for Damage Detection

   L. Pinello, O. Hassan, M. Giglio, C. Sbarufatti

   Aerospace, 11(3), 222 · 2024

7. Convolutional autoencoders and CGANs for unsupervised structural damage localization

   R. Junges, Z. Rastin, L. Lomazzi, M. Giglio, F. Cadini

   Mechanical Systems and Signal Processing, 220, 111645 · 2024

8. Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach

   A. Gonzalez-Jimenez, L. Lomazzi, R. Junges, M. Giglio, A. Manes, F. Cadini

   Structural Health Monitoring, 23(3), 1514-1529 · 2024

9. Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

   M. Parziale, P. Henrique Silva, M. Giglio, F. Cadini

   Structures, 69, 107583 · 2024

10. Physics-Informed Neural Networks for the Condition Monitoring of Rotating Shafts

    M. Parziale, L. Lomazzi, M. Giglio, F. Cadini

    Sensors, 24(1), 207 · 2024

11. Temperature enhanced early detection of internal short circuits in lithium-ion batteries using an extended Kalman filter

    Y. Jia, L. Brancato, M. Giglio, F. Cadini

    Journal of Power Sources, 591, 233874 · 2024

12. Unsupervised data-driven method for damage localization using guided waves

    L. Lomazzi, R. Junges, M. Giglio, F. Cadini

    Mechanical Systems and Signal Processing, 208, 111038 · 2024

13. Towards a stochastic inverse Finite Element Method: A Gaussian Process strain extrapolation

    D. Poloni, D. Oboe, C. Sbarufatti, M. Giglio

    Mechanical Systems and Signal Processing, 189, 110056 · 2023

14. Variable Thickness Strain Pre-Extrapolation for the Inverse Finite Element Method

    D. Poloni, D. Oboe, C. Sbarufatti, M. Giglio

    Sensors, 23(3), 1733 · 2023

15. A fuzzy-set-based joint distribution adaptation method for regression and its application to online damage quantification for structural digital twin

    X. Zhou, C. Sbarufatti, M. Giglio, L. Dong

    Mechanical Systems and Signal Processing, 191, 110164 · 2023

16. On the explainability of convolutional neural networks processing ultrasonic guided waves for damage diagnosis

    L. Lomazzi, S. Fabiano, M. Parziale, M. Giglio, F. Cadini

    Mechanical Systems and Signal Processing, 183, 109642 · 2023

17. Towards a deep learning-based unified approach for structural damage detection, localisation and quantification

    L. Lomazzi, M. Giglio, F. Cadini

    Engineering Applications of Artificial Intelligence, 121, 106003 · 2023

18. Analytical and empirical methods for the characterisation of the permanent transverse displacement of quadrangular metal plates subjected to blast load: Comparison of existing methods and development of a novel methodological approach

    L. Lomazzi, M. Giglio, A. Manes

    International Journal of Impact Engineering, 154, 103890 · 2021

19. Numerical study on the dynamic progressive failure due to low-velocity repeated impacts in thin CFRP laminated composite plates

    M. Rezasefat, A. Gonzalez-Jimenez, M. Giglio, A. Manes

    Thin-Walled Structures, 167, 108220 · 2021

20. Ballistic strain-rate-dependent material modelling of glass-fibre woven composite based on the prediction of a meso-heterogeneous approach

    D. Ma, A. Manes, S. Amico, M. Giglio

    Composite Structures, 216, 187-200 · 2019

21. Experimental tests and numerical modelling of ballistic impacts against Kevlar 29 plain-woven fabrics with an epoxy matrix: Macro-homogeneous and Meso-heterogeneous approaches

    L. Bresciani, A. Manes, A. Ruggiero, G. Iannitti, M. Giglio

    Composites Part B: Engineering, 88, 114-130 · 2016

All group publications

• Lecturer

  Advanced Machine Design

  MSc Mechanical Engineering · 10 CFU · 2nd semester · Milano Bovisa

  Course sheet

Courses of 2026/2027, from the Politecnico’s course sheets. All the group’s courses

• Digital TwinsPHMEnginesTURBOMON

  Development of an engine digital-twin

  Optimising maintenance policies is a complex task pushing towards a shift from programmed maintenance to condition-based maintenance. An engine digital twin is thus suitable to monitor and keep track of the health condition throughout the lifespan of the engine, enabling the prediction of engine health state by implementing diagnostic and prognostic algorithms enabling conditions-based maintenance policies.

  Details and apply
• Digital TwinsPHMAIReinforcement LearningEnginesTURBOMON

  Engine fleet logistic digital twin for life cycle management optimisation

  Optimising maintenance policies is a complex task pushing towards a shift from programmed maintenance to condition-based maintenance. This task is fundamental when dealing with aircraft fleets, developing a Discrete Event Simulator (DES) to simulate numerous fleet lifecycles and implement Reinforcement Learning algorithms for automatic decision making.

  Details and apply
• SHMPHMMachine LearningHelicoptersSAMAS 2

  Data-based monitoring for impact/corrosion damage identification in helicopters

  In the framework of SAMAS 2, flight tests with real helicopters are performed to evaluate the health status through a specifically designed Structural Health Monitoring and Prognosis system. The data from the flight tests are used to develop diagnostic algorithms capable of identifying damage and the degradation level of the structure.

  Details and apply

All thesis topics