# Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
## 1 Introduction  
Computer numerical control (CNC) machine tools have been widely used in high speed and precision machining, such as curved surface, gears, aerospace materials, integrated circuit (IC) parts, and precision components, etc. This is why machine tool has been focused on how to accomplish high-speed and high-precision machining. Accurate control is required for machine tools to follow the desired contours. With increasing requirements for precision operations, many control strategies have been proposed [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. For industrial applications, the proportional-integral-derivative (PID) controllers of each axis are used for feed drive systems, therefore the tuning method for multi-axis synchronization for precision results are required. However, system dynamic parameters may be uncertain or vary with time. Thus, the system identification of feed drive systems should be done before tuning parameters. Some literature introduce virtual machine tools, including virtual CNC, servo models, and mechanical dynamics of feed drive systems, for treating the problem [12; 13; 14; 15; 16; 17; 18; 19].  
Feed drive systems may have unstable results or poor performance when servo parameters are not designed properly [20; 21]. Conventionally, the control gains of position and velocity controller are set as high as possible, under the condition that the stability and robustness of the closed loop system is secured with some stability margins [21; 22]. In general, each commercial CNC machine tool guides the user for tuning parameters step-by-step according to expert experience. In this study, we propose an intelligent and efficient method for the servo tuning problem to improve synchronization.  
In order to employ the intelligent efficient method, this study developed a virtual CNC feed drive system via system identification technology using Heidenhain TNopt software. This can obtain the characteristics of the system and the mathematical models which we can use in numericalsimulations; therefore, we were able to implement this intelligent and efficient method by an optimization algorithm, e.g., a genetic algorithm, particle swarm optimization (PSO), evolutionary algorithm, electromagnetism-like algorithm, etc. [23; 24; 25; 26; 27; 28; 29; 30]. In this study, the PSO was adopted for solving optimization problems. As a result, combined with feed system signals processing and spectrum analysis, the servo system with mechanical dynamics was established. The intelligent algorithm was used to automatically generate a set of suitable servo parameters for the current machine tool. After optimization, performance and processing quality would be better than the original. The proposed approach contained a system identification phase and a servo tuning phase based on the same bandwidth for all axes feed drive systems. The particle swarm optimization algorithm was adopted to obtain the system parameters and maximize the corresponding bandwidth. An efficient two-step servo tuning method based on gain and phase margins was proposed for high speed and precision requirements. All feed drive systems controller gains were optimized simultaneously for synchronization. Finally, the cloud computing system, called Machine Dr., was established for system identification, servo tuning, and monitoring.  
This organization of this paper is introduced as follows. Section 2 introduces the preliminaries, including the modeling of feed drive systems, specification of machine tools, and the particle swarm optimization algorithm. The proposed system identification based on PSO is introduced in Section 3. Section 4 introduces the intelligent servo tuning technique by two-step PSO. The corresponding simulation, experimental results, and the Machine Dr. system are shown in Sections 3 and 4. Finally, the conclusion is given.