## 5 Practical Application to the Real Machine  
Monitoring the machining status is very important in managing the machined part quality during the machining process [27; 67; 68; 69; 70; 71; 72]. For applying the proposed CPS concept to areal case in manufacturing field, the self-adjusting cutting condition on a high-speed machining machine was focused on. Figure 17 shows the disturbance classification by which the disturbances were sorted into internal and external disturbances. External disturbances related to an order change or the supplier. Other disturbances such as tool wear, incorrect process and so on relate to physical system, process, and service. These disturbances were recognized by their symptoms which are used for developing the rules or reasoning mechanism in prediction for CPS to find out the disturbance. According to the disturbance classification, the database for CPS including information such as resource, disturbance, and recovery method for each disturbance respectively were developed. In the machining system, each machine was controlled by the corresponding machine agent as shown in Figure 18. To realize the perception function, the KEPServerExOPC was used to collect data on the shop floor. The disturbances such as the high temperature of oil or cooling system could be  
Figure 16: Experimental results about the response time of the system. Figure 17: Disturbance classification. recognized directly through the change of data value assigned in the OPC data. Some disturbances required the use of pattern recognition algorithms such as artificial neural network (ANN) to recognize the disturbance. In this case, the perception module was extended to diagnose the disturbances. Experimental study was carried out. Contour profile milling by flat end mill tool without coolant was done on the high-speed vertical machining center-HS Super TC500 as shown in Figure 18. The work-piece was AISI 1043 carbon steel because this is a common material for mechanical engineering. The cutting tool selected was TiAlN-Sandvik, flat end mill, diameter = 20 mm, four teeth. Based on the theoretical study of the high-speed cutting process and the factors affecting the machining response, the experiments were carried out with the input cutting parameters as follows:  
* Cutting speed on the high-speed milling machine \(v\) (m/min);
* Radial cutting depth \(a_{r}\) (mm);
* Axial cutting depth (\(a_{p}\)): 10 mm;
* Feed rate \(f\) (mm/min). According to experimental planning, the ranges of input cutting parameter are as follows: \(370\leq v\leq 595\) (m/min); \(2357\leq f\leq 3790\) (mm/min); and \(0.1\leq a_{r}\leq 0.95\) (mm). During machining process, cutting tool wear directly impacts surface roughness and is a key factor in determining whether the machined parts meet specifications. So, determining the amount of tool wear is very important and necessary. Currently, cutting parameters such as cutting depth, cutting speed, and feed rate are optimized before machining. This optimization method is called static optimization. Static optimization generates optimal cutting parameters using a static model of the machining process. A disadvantage of this method is that it does not consider dynamics of the cutting process. After generating the optimal cutting parameters, these parameters are used for the machining part on the machine tool. During the machining process, these parameters cannot be adjusted. This research proposes the optimizing control of CPS during the machining process in order to ensure the quality of the machined part. This approach is called a dynamic optimization. Different to static optimization, the dynamic optimization enables the generation of the new cutting conditions in  
Figure 18: Machine tool controlled by the machine agent. consideration of the current state of the machining process, such as tool-wear. Then, the new optimal cutting condition will be sent to the machine tool to ensure the quality of the machined part in the allowed limit.