# Parameters Adjustment Optimization for Prepreg of Copper Clad Laminate Based on Virtual Metrology
## 5 Experimental Results  
### Data Description  
This research used real-world data collected from the prepreg manufacturing department of a plastic company in Taiwan. The demand for different products varies widely in the company. For the products with high demand, as the manufacturing data has been studied for a long time, the engineers can adjust input values very well in the light of their experience. Moreover, these high demand products are always assigned to specific production lines. Thus the number of setups can be reduced and increase the efficiency of these production lines. There are many different types of products with low demand, but the corresponding manufacturing data is relatively sparse. Therefore, engineers lack experience in setting up these product types. These low demand products are also assigned to specific production lines. The both number of setups and setup time are very high, and this reduces efficiency of these production lines. This research focuses on one production line which always produces low demand product types. The collected input and output variables are listed in Table 1. There are 12 input variables and 2 output variables. Among input variables, moving speed is the transfer speed of the glass cloth,which affects the heating time in the oven. Epoxy resin density is the density of resin which is the raw material of the impregnation process. There were two oven temperatures, as shown in Figure 1; one was the temperature of the oven on the left-hand side and the other was the temperature of the oven on the right-hand side. There were two doctor rollers, the positions of which affected the volume of resin that adhered to the glass cloth. Moreover, there were four fan speeds, as shown in Figure 1. The first was located on the upper left for moving air into the oven, the second was located on the lower left for moving air out of the oven, the third was located on the upper right for moving air out of the oven, and the fourth was located on the lower right for moving air into the oven. Revolution speed was the revolution speed of both doctor rollers. Gel time 1 indicated the gel time of the epoxy resin. It was the time required for resin to become solid at a certain temperature. It can be noted that among input variables, epoxy resin density, oven temperature 1, oven temperature 2, and gel time 1 were hard to adjust. Epoxy resin density and gel time 1 were the attributes of the epoxy resin, and the epoxy resin was made before it been put into the trough. Therefore, it was impossible to adjust epoxy resin density and gel time 1. Both oven temperatures were increased by electric energy and cost was required, but both oven temperatures were reduced by itself and time was required. Therefore, engineers always want the temperature stay the same. Regarding the output variables, the ratio of resin was the ratio of resin weight in prepreg, and gel time 2 indicated the gel time of the resin in prepregs. All instruments were well integrated, and almost input variables could be monitored and controlled by a central control system. However, epoxy resin density, gel time 1, ratio of resin, and gel time 2 could only be measured by manual labors. Every time when new parameters were set, the four variables were entered into the system manually. Moreover, all of the instruments were calibrated regularly. This research collected the manufacturing data of a production line which focuses on producing low demand products. The products which are produced by a certain type of glass cloth and a certain formula of epoxy resin were collected. From January 2018 to January 2019, 216 sets manufacturing data were collected. The maximal and minimal values of all input and out variables are shown in Table 1. Although the collected 216 sets of data were based on a single production line, type of glass cloth, and formula of epoxy resin, the corresponding specifications were quite different because they respond to customer needs. Consequently, the target values of the collected data were not the same.