Tesi etd-03272025-154552 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
VERONA, LEONARDO
URN
etd-03272025-154552
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
Relatori
relatore Prof. Fanteria, Daniele
correlatore Prof.ssa Furtado Pereira da Silva, Carolina
tutor Danzi, Federico
correlatore Prof.ssa Furtado Pereira da Silva, Carolina
tutor Danzi, Federico
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
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.
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.
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