# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 3 Method
### Penetration Simulation and Data Set Introduction  
#### 3.1.1 Selection of the Penetration Overload Data Extraction Position  
In this paper, a deep learning model is used to integrate the parameters of penetration overload and penetrating ammunition to train on existing simulation data and experimental data. The parameters related to penetration ammunition, such as the weight, length and angle of attack, are set in advance. We combine these parameters with the overload curve of the short-term target impact and the calculated speed and incorporate them into the prediction model. The model transfers the predicted penetration overload curve to the fuze as a priori detonation control data. A data set is obtained through simulation and live ammunition experiments. In the simulation model, the bullet body and its bottom bolt are made of high-strength alloy steel (30 CrMnSiNi2A) and the * MAT_PLASTIC_KINEMATIC constitutive model. This model can adopt the mixed hardening mode or the isotropic hardening mode. The isotropic hardening mode is adopted in this paper. The strain rate effect of this model is expressed by the Cowper-Symonds model. During the high-speed penetration process, the concrete is under large strain, high pressure and a high strain rate, so * MAT is adopted in this paper with the _JOHNSON_HOLMQUI ST_CONCRETE constitutive model, or HJC constitutive model for short. The overload curve of the fuze simulation and test is greatly affected by vibration, and the oscillation and aliasing phenomena are serious. The penetration overload data at the missile body are selected as the data set. Figure 1 shows the simulated penetration overload curve extracted from the fuze, and Figure 2 shows the simulated penetration overload curve extracted from the missile body, where the sampling interval is 20 \(\upmu\)s. In Figure 1, the overload signals extracted from the fuze end oscillate and mix severely, and it is impossible to distinguish the overload change during penetration. The overload curve extracted from the missile body in Figure 2 obviously changes, which can clearly reflect the penetration process.  
Figure 1: Simulation overload curve of multilayer target fuze terminal.  
3.1.2. Selection of the Penetration Overload Data Set Label Type  
The data set is composed of simulation data and experimental data. The data set is divided into a training set, verification set, and test set according to a certain ratio to ensure mutual exclusion between the training set, verification set, and test set; this is commonly known as the \"reserve method\". The reserve method usually divides large data-sets into sections of 90% for training, 5% for verification and 5% for testing, and small data sets are divided into 60%, 20% and 20% proportions. Due to the limited size of the data set, the data set is divided into a 60% training set, a 20% verification set and a 20% test set. In Figure 3, the data set labels of multivariate time series prediction are composed of time, bullet length, bullet weight, speed, angle of attack, and overload information from left to right. Overload is the target data category to be predicted. Furthermore, the data set contains overload data for multilayer targets and thick targets.