The transformation of the aeronautical industry towards sustainable and cost-effective manufacturing is essential for enhancing aircraft performance while reducing environmental impacts and production costs. This study integrates Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and machine learning to enhance sustainable design in aeronautics. A Multi-disciplinary Optimization (MDO) approach was applied to a composite airframe panel, revealing that increased panel mass elevates the impacts of Climate Change (CC) and Resource Use (fossils), largely due to carbon fiber and energy-intensive manufacturing. A Random Forest model predicted LCA/LCC outcomes, facilitating real-time, sustainability-driven decisions. Optimization reduced environmental impacts by 15%. Recommendations include bio-based composites and renewable energy use to further lower environmental costs.

In this paper, we introduce an advanced AI-based solution for predicting structural damage in aircraft laminates. Our innovative approach focuses on detecting and locating fatigue damage within composite structures, thereby enhancing the assessment of aircraft health and usage. By leveraging state-of-the-art ultrasonic guided wave (UGW) inspection simulations of composite laminates integrated with piezoelectric transducers, comprehensive datasets are extracted efficiently. The signals captured by the piezoelectric sensors are utilized to engineer key features sensitive to composite structural damage, which are then used to train a deep neural network (DNN) for accurate structural damage prediction. Our findings demonstrate the significant potential of combining advanced simulation techniques with machine learning to improve the accuracy and reliability of structural health monitoring in aerospace applications.

Ultrasonic Guided Wave (UGW)-based Structural Health Monitoring (SHM) is a promising strategy for detecting damage to aeronautical structures, although its application is complicated by signal complexity and experimental uncertainty. This work seeks to identify damage-sensitive signal features for integration into Machine Learning (ML) frameworks, offering physics-informed indicators. The study combined experimental monitoring of damage to Carbon Fibre Reinforced Polymer (CFRP) plates and finite element models. To overcome the numerical–experimental mismatch, an ML algorithm predicted experimental characteristics from numerical data. The robustness of the model was validated by extrapolation (prediction of future damage) and generalization (prediction on unseen plates) strategies, confirming that ML can robustly correct for uncertainty. These results validate hybrid strategies that feed Digital Twin approaches to structural diagnosis and real-time forecasting.