In a significant stride towards enhancing the safety and efficiency of maritime structures, researchers have developed a novel approach combining numerical simulations and machine learning to evaluate the ballistic performance of aluminum structures. This work, led by Tota Rakesh Kumar from the School of Marine Engineering and Technology at the Indian Maritime University in Chennai, offers a promising tool for designing lightweight, high-performance protective structures that can withstand high-velocity impacts.
The study, published in ‘Results in Engineering’ (translated to English as ‘Results in Engineering’), focuses on understanding and predicting the ballistic response of aluminum plates when impacted by rigid steel projectiles. The research is particularly relevant to the maritime sector, where the integrity of structures under extreme conditions is paramount.
Kumar and his team conducted finite element method (FEM) simulations using ABAQUS/Explicit scheme, incorporating the Johnson-Cook constitutive and failure models. These simulations evaluated the energy absorbed during impact, with results closely matching experimental data, showing an average deviation of less than 7%. This accuracy is crucial for ensuring the reliability of predictive models in real-world applications.
To address the computational costs and complexity associated with FEM, the researchers developed supervised machine learning (ML) models, including Support Vector Regression (SVR), Random Forest (RF), Decision Tree (DT), and Multi-Layer Perceptron (MLP). These models predicted absorbed energy based on projectile geometry, impact velocity, and target thickness. The ML-based models demonstrated strong predictive accuracy, offering a more efficient alternative to traditional simulation methods.
“The integrated FEM-ML framework proposed herein demonstrates enhanced predictive accuracy and computational efficiency,” Kumar explained. This framework not only provides rapid ballistic performance evaluation but also aids in the optimization of impact-resistant designs, which is particularly valuable for maritime applications.
The commercial impacts of this research are substantial. Maritime industries can leverage these findings to design more robust and lightweight structures, reducing material costs and improving overall performance. The ability to quickly and accurately predict the ballistic response of materials can lead to safer and more efficient ship designs, particularly for vessels operating in high-risk areas.
Moreover, the insights gained from detailed analysis of fracture patterns under different impact scenarios offer additional benefits. Understanding failure mechanisms can inform better material choices and structural designs, further enhancing safety and durability.
For maritime professionals, this research opens up new opportunities for innovation and improvement in protective structures. The integration of machine learning with traditional simulation methods provides a powerful tool for rapid evaluation and optimization, ultimately leading to more resilient and cost-effective solutions.
As Kumar noted, “The detailed analysis of fracture patterns provides additional insights into failure mechanisms under different impact scenarios.” This understanding is invaluable for maritime engineers and designers seeking to create structures that can withstand extreme conditions.
In summary, the work by Tota Rakesh Kumar and his team represents a significant advancement in the field of ballistic performance evaluation. By combining FEM simulations with machine learning, they have developed a framework that offers both accuracy and efficiency. This approach holds great promise for the maritime sector, enabling the design of safer, more efficient, and more cost-effective structures. The research, published in ‘Results in Engineering’, underscores the potential of integrating advanced computational methods with machine learning to address real-world challenges in maritime engineering.