Master Thesis
If you want to work with us, contact Prof. M. Giglio for a Master Thesis
or a Ph.D. application. Herein the list of the available Master Thesis.
Thesis are effective research priorities of the Research Team and their
present availability may change over time. If you want to know more
details please download our presentation about the Master Thesis
available.
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the list of Master Thesis available with SIGMALab
Master Thesis available:
Uncover the uncertainty effect for composite
materials
Investigation on the interface of composite materials (with and
without nanofillers)
Long term durability of woven fabric composites subjected to impact
loads
Application of machine learning in prediction of low-velocity impact
response of hybrid composites
Performance evaluation of thermoplastic and thermoset woven fabric
composites under impact loadings
Modelling of nanocomposites
Double-Double, a new family of composite laminates
Design and analysis of structural batteries
Development of a methodological approach to describe the
vulnerability of platforms subjected to complex threat mechanisms.
Development of a methodological approach to the multidisciplinary
topological optimisation of protections.
Numerical characterisation of the behaviour of blast loaded
composite structures
Numerical characterisation of blast loaded structures and
development of machine learning-based surrogate models.
Numerical modelling of helmet under high velocity and blunt
impacts
Development of machine learning methods to improve the fidelity of
numerical models for simulating ultrasonic guided waves in solid
media.
Investigation on the effect of filament winding pattern modelling
parameters on the prediction of delamination.
Mechanical behaviour of type 4 Hydrogen pressure vessel under random
extreme loadings.
Analysis of Guided waves (GWs) propagation in cylindrical composite
vessels
Model-based structural health monitoring in composite pressure
vessels.
Sensor network optimization and SHM performance evaluation for the
monitoring of a bond repair patch
Design and implementation of an SHM system for an operating
helicopter
Development of a Discrete Event Digital-Twin of a Naval Fleet for
Condition-Based maintenance
Development of models for corrosion rate prediction
Development of models and algorithms for corrosion damage assessment
Development of models for a cracked rotor shaft
Model-based structural health monitoring in transmission shaft.
Development of models and algorithms for impact damage assessment
Signal processing and data analysis for impact damage assessment
Inverse FEM and Smoothing Element Analysis (SEA) performance
optimization for shape sensing
Statistical damage diagnosis and prognosis of a cracked structure
with inverse FEMCensored Gaussian Process Regression for
non-parametric Bayesian Fatigue Life Estimation
Manoeuver classification and clustering from experimental strain
data using Convolutional Neural Networks and Wavelet Transforms
Probabilistic force localization and reconstruction using a
Reversible Jump Markov-Chain Monte Carlo Method
Scalable Hamiltonian Monte Carlo via Surrogate Methods for
Digital-Twins
Multivariate Gaussian Process strain extrapolation for iFEM
uncertainty quantification
Physics-Constrained nonstationary Gaussian Process
Development of a supervised and/or unsupervised deep learning-based
framework to perform damage diagnosis based on vibration measurements
(transmissibilities)
Development of supervised deep learning-based methods for damage
diagnosis on composite and hybrid plates
Development of supervised deep learning-based methods for damage
diagnosis on composite and hybrid plates
Development of unsupervised deep learning methods for damage
diagnosis on composite and hybrid plates
Development of deep learning methods for damage diagnosis exploiting
data fusion techniques
Efficient and explainable deep graph neural networks to numerically
simulate complex phenomena
Physics-informed deep neural networks to account for the physics
governing ultrasonic guided waves propagation and interaction with
damage
Surrogate modelling of a lunar rover Digital Twin for real-time
operations
Integration of anomalies and degradations in a lunar rover Digital
Twin for performing damage diagnosis
Development of a Digital Twin of a lunar rover Heat Rejection System
for performing damage diagnosis
Development of a Digital Twin of a lunar rover power harvesting and
storage system
Lithium-ion batteries PHM by exploiting mechanical-based
measurements and machine learning
Offering an achievable and advanced health monitoring strategy for
structural batteries