## 1 Introduction
In reference [21], two novel loss functions are proposed to train the feed-forward artificial neural network (ANN), and the differential relationship between position and velocity mapping is incorporated into the forward kinematics model of the robot structure, the study shows that the introduction of velocity mapping can improve the adaptability of the learning model to the control task. In reference [22], a novel feed-forward artificial neural network (ANN) structure is proposed to learn the complete pose of the robot in SE (3), and the difference relation is introduced into the learning process. The research shows that the proposed method can correctly model the pose of the robot. In reference [23], the authors do not consider reinforcement learning or trial and error methods. Reinforcement learning is to apply these mapped actions, get a series of feedback reward values, and then select the action with the largest reward value. When the degree of freedom of the robot increases,  
Figure 1: The steel bar finishing process. the convergence performance of the controller will be significantly worse. As you get more and more degrees of freedom, it takes longer and longer to converge. In reference [24], the transfer learning method was applied to train neural networks, which greatly reduced the number of training images required. In reference [25], a new robust return to zero neural networks (RZNN) model was proposed to study the timing trajectory tracking control of wheeled mobile robots in a noise pollution environment. Neural networks have many other applications as well. In reference [26], The finite-time stabilization problem of complex-valued neural networks with proportional delays and inertia terms is studied. In reference [27], an improved convolutional neural network (CNN) was developed for fault feature extraction and classification of hydraulic piston pumps. The DNN method proposed in this paper is a deep learning method included in ANN. In this paper, literature [19] is introduced to better understand the feasibility of the DNN method proposed. DNN is more capable of modeling or abstracting things and can simulate more complex models. The problem of inverse kinematics of grinding robots is characterized by high computational complexity and time consumption. Hence, it is necessary to introduce DNN into the inverse kinematics of grinding robots. In order to more effectively control the trajectory of the grinding robot and correctly solve the forward and inverse kinematics parameters of the robot, and to avoid the complicated process of solving kinematics equations and the un-uniqueness of inverse kinematics solutions, to achieve fast and efficient online grinding operation of bar grinding robot, in this paper a method based on the multi-layer forward propagation deep neural network is proposed. The proposed method is used to solve the forward and inverse kinematics and realize the kinematics calculation and is compared with the traditional analytical calculation method. It is demonstrated that not only the complicated mathematical theoretical derivation calculation process can be avoided, but results with higher accuracies can also be achieved, it can achieve the goal of fast, efficient, and accurate online grinding operation of bar grinding robot. This paper takes KUKA KR series industrial robot (grinding robot) as the research object, the main structure of the article is arranged as follows: in Section 2, the basic concepts and related theories of the grinding robot process and kinematics are introduced. A strategy for solving grinding robot kinematics equations based on a multi-layer forward propagation deep neural network is proposed in Section 3. In Section 4, the experimental scheme is stated, and the main simulation experiments and optimization are described in detail. In Section 5 some conclusions are drawn.