Tipo di tesi
Tesi di laurea magistrale
Titolo
Computationally-Based Surrogate Models for the Rapid Prediction of Residual Compressive Strength in Impact-Damaged Composite Laminates
Dipartimento
INGEGNERIA CIVILE E INDUSTRIALE
Corso di studi
INGEGNERIA AEROSPAZIALE
Parole chiave
- composite laminates
- compression after impact
- high-fidelity simulations
- impact damage
- low-velocity impact
- machine learning
- residual compressive strength
Data inizio appello
15/04/2025
Consultabilità
Non consultabile
Data di rilascio
15/04/2095
Riassunto (Italiano)
The focus of this thesis is on improving methodologies for evaluating impact-induced damage in high-performance composite laminates.
In particular, it develops a framework that uses machine learning and high-fidelity simulations to quickly estimate the residual compressive strength of damaged laminates. The proposed method enables rapid, non-destructive assessment of structural integrity, allowing for more accurate scheduling of repair interventions and inspection intervals. Consequently, the maintenance strategies for composite structures are tailored, resulting in a reduction in aircraft downtime and a significant economic advantage for the aerospace industry.
