## 2 Literature Review
[21] noted how AVM technology had been applied to various industries to convert the offline sampling inspection with metrology delay into online and real-time total inspection. With the assistance of AVM, each workpiece of carbon fiber products can be inspected to make sure that a comprehensive quality inspection has taken place. In 2017-2018, [PERSON] [22; 23; 24] mentioned how the concept of the interaction effect is explained in physics as being when two or more independent variables exist in an experimental study, and how the effect of one of the independent variables exhibits an alternating phenomenon on the other independent variable. The true effect of some factors is changed by another factor. The VSM method in [PERSON]'s method is used to evaluate this interaction effect. In 2019, [PERSON] [25] reviewed various methods to evaluate the performance of five-axis machine tools through identification and multi-type comparison, with the RPE being the most practical. RPE is currently one of the research methods that can obtain accurate reference data with both a quantitative and identifying approach and is one of the evaluation methods for multi-type five-axis machine tool methods. Based on the interface of RPE and IT level distribution in general mechanical design change, this paper attempted to introduce fuzzy theory to obtain excellent research results. This study calculated the attribution level of the tested items. With regards to the conflicts in the re-evaluation of the performance of various types of five-axis machine tools, the research method provided a direct differentiation and defuzzification of the attribution reduction interval, where the predicted results can be directly judged and evaluated. In 2020, [PERSON] [26] quantified the interactions between the warpage directions of glass fibers. This method provided reference data along with a simple method to determine whether it affects the mold, depending on whether glass fiber was added, and the holding pressure time conditions. Regarding the compression effect on the screw at the front end of the barrel, during the injection and holding process in the virtual simulation, the material in the barrel and nozzle of the injection machine displays transient compression behavior; the integrated machine response parameter method with the simulation of the material compressibility effect of the polymer molten plastic injection pressure is rarely mentioned in the related literature. The traditional VM technology only considers a single phase change in the machine settings, so the prediction of the difference in molten plastic pressures will be different from the experimental values. Considering the machine integration and the barrel compression effect, the injection pressure prediction curve could be greatly improved, whereby the simulated predicted value is closer to the actual value, and the machine integration prediction is closer to the actual settings. Data is key to this process. In 2020, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [27] tried to combine a classical 1D twin-screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. The used machine learning techniques with low data limit were used as a correction for the simulation.