Menu

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.

 

DOWNLOAD 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