# Prediction of Manufacturing Quality of Holes Based on a BP Neural Network
## 1 Introduction  
Carbon fiber-reinforced plastic (CFRP) has several advantages such as light weight, high specific strength, corrosion resistance, and good vibration absorption. It is widely used in the aerospace, automotive, and civil engineering industries, among other fields [1]. Based on the orthogonal to oblique cutting transformation, a thermo-mechanical finite element (FE) model was established to simulate the conventional drilling and ultrasonic vibration drilling processes, and drilling-induced damage was predicted by [PERSON] et al. [2]. [PERSON] et al. [3] constructed a CFRP/Al three-dimensional drilling finite element simulation model by using the finite element software Abaqus. The tearing, delamination damage, burr formation characteristics, and mechanism of carbon fiber composites during CFRP/Al drilling were simulated. [PERSON] et al. [4] explored an intelligent control model for the processing of composite materials. A series of drilling tests were carried out on CFRP laminates by using machining centers, and the key process parameters under various cutting conditions were determined. [PERSON] et al. [5] presented a new comprehensive approach to select cutting parameters for damage-free drilling in carbon fiber-reinforced epoxy composite material. Hybrid composites, composites with toughthermoplastic resins, modified matrices, surface modification of fibers, translaminar reinforcements, and interlaminar modifications such as interleaving, short fiber reinforcement, and particle-based interlayer were discussed by [PERSON] et al. [6]. [PERSON] et al. [7] comprehensively analyzed the delamination of different types of bits, and the critical thrust at the beginning of the stratification was predicted and compared with the twist drill. [PERSON] et al. [8] introduced a generic and improved model to simultaneously predict the conventional cutting forces along with three-dimensional (3D) surface topography during a side milling operation. An improved technique to calculate the instantaneous chip thickness was also presented. [PERSON] et al. [9] proposed a methodology for predicting the cutting forces by transforming specific cutting energies from orthogonal cutting to oblique cutting. The results showed that the method can predict the cutting forces in helical end milling of unidirectional and multidirectional composites, as well as over the entire range of fiber orientations from 0\({}^{\circ}\) to 180\({}^{\circ}\). [PERSON] et al. [10] employed uncoated carbide and two diamond-coated carbide drills with different drill tip angles in drilling experiments of aerospace-quality thick fabric woven CFRP laminates. [PERSON] et al. [11] used a staggered tool to mill carbon fiber, studied the influence of cutting radius on tool wear, found the influence of cutting radius on milling force and surface quality, and discussed the formation mechanism of three-dimensional surface topography. The back propagation (BP) neural network algorithm is a new type of artificial intelligence technology, which achieved good results in performance prediction, process optimization, etc. [PERSON] [12] proposed a robust design method for predicting the neural network model of a carbon fiber-reinforced epoxy resin (BMS 8-256) drilling layer, damage width, and hole surface roughness. The method was based on parametric analysis of the neural network model, using experimental design methods to accurately predict process-induced damage. [PERSON] et al. [13] focused on the effects of the drilling process parameters on the delamination behavior at the inlet of carbon fiber composites. An artificial neural network model with spindle speed, feed rate, and point angle as parameters was established, and high-speed drilling stratification analysis was carried out. The analysis demonstrated the advantage of using a higher speed to control stratification during drilling.