dataset_dop_prediction

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.

本数据集包含基于仿真生成的数据，用于承受高速载荷工况的陶瓷材料体系的冲击响应分析与预测。数据集涵盖核心参数：冲击速度、材料属性、试样厚度等，以及实测响应变量，例如侵彻深度（Depth of Penetration, DoP）。本数据集旨在支撑基于机器学习的建模研究，并为理解冲击行为的内在力学机制提供可复现的输入数据。所有数据均经过处理与结构化规整，可直接用于数据驱动的建模流程。本数据集关联的研究论文题为《基于机器学习算法的不同弹道威胁下装甲陶瓷侵彻深度预测》（原英文标题："Depth of Penetration Estimation of Armor Ceramics Under Different Ballistic Threats by Using Machine Learning Algorithms"），现已公开上线，以保障研究的透明度与可复现性，并推动冲击工程领域的后续研究。