dataset_dop_prediction
Description
This dataset contains simulation based data used for the analysis and prediction of impact response in ceramic material systems subjected to high rate loading conditions. The dataset includes key parameters such as impact velocity, material properties, thickness, and measured response variables such as depth of penetration (DoP). It was developed to support machine learning-based modelling and to provide reproducible input data for understanding the underlying mechanics of impact behaviour. All data have been processed and structured for direct use in data-driven modelling approaches. The dataset is associated with the study titled "Depth of Penetration Estimation of Armor Ceramics Under Different Ballistic Threats by Using Machine Learning Algorithms" and is made publicly available to ensure transparency, reproducibility, and further research in impact engineering.
Files
Steps to reproduce
The dataset was generated using numerical simulations performed in LS-DYNA to investigate impact response under high strain-rate conditions. The material models were validated against experimental data from the literature to ensure accurate representation of material behaviour. Simulation outputs, including depth of penetration (DOP) and related response variables, were extracted and structured into a consistent dataset. Machine learning models were then developed in Python using standard libraries to predict impact response. Model performance was evaluated using appropriate metrics (e.g., R², MSE), and the dataset was used to enable data-driven prediction of DOP.