It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V₅₀ ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity (BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network (GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V₅₀ BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process.

重要的是要了解弹道材料如何响应弹丸的冲击，以便在防护装甲系统的设计过程中做出明智的决定。弹道测试是一个基于标准的过程，对材料进行测试以确定它们是否符合保护、安全和性能标准。对于V ₅₀在弹道测试中，射弹以不同的速度发射以确定称为弹道极限速度 (BLV) 的关键设计参数，在该速度以上射弹会穿透目标。然而，这些测试本质上是破坏性的，因此可能会产生相当大的相关成本，尤其是在研究复杂的装甲材料和系统时。这项研究使用最近一类称为生成对抗网络 (GAN) 的机器学习系统提出了一个独特的解决方案。GAN 可用于生成新的弹道样本，而不是执行额外的破坏性实验。GAN 网络架构在三个不同的弹道数据集上进行了测试和训练，并比较了它们的性能。V ₅₀ BLV 在每种情况下的误差小于 5 %。结果表明，可以在有限数量的弹道样本上训练生成网络，并使用经过训练的网络生成许多代表其训练数据的新样本。该论文重点介绍了生成网络可以为弹道应用带来的好处，并在设计过程的早期阶段提供了一种替代昂贵测试的方法。