## 1 Introduction
After the data is processed and analyzed, valuable information can be obtained from the manufacturing process, production system, and equipment. These technologies achieve advanced product manufacturing quality prediction, which replaces the previous postinspection method of production quality. It can discover and eliminate hidden quality hazards in advance and effectively reduces the cost of enterprise quality control. Manufacturing quality prediction also provides data support for reliability evaluation and parameter optimization, thereby improving the enterprise's intelligent management level. However, the actual use of these solutions requires a high degree of professional knowledge and computer-programming capabilities. The current small-batch production types and rapid changes have brought new challenges to the realization of accurate quality prediction. With the rapid development and gradual maturity of artificial intelligence and machine learning, a new effective method, which uses a learning algorithm and theoretical development, is provided for research in high-dimensional, small-sample quality prediction. Supervised learning is most widely used method in machine learning, and it forms predictions through learning mapping [7]. Research on machine learning algorithms for quality regression prediction problems has become a great concern. The main task of the regression problem is to train the learner according to the existing data and map the input to the corresponding output result to achieve the purpose of prediction. GE and others analyzed the application of 10 kinds of supervised machine learning methods in the manufacturing process, including partial least square regression (PLS), random forest, artificial neural network (ANN), support vector machine (SVM), back propagation (BP) neural network algorithm, and decision tree [8; 9]. Some researchers also use heuristic algorithms and machine learning model combination construction methods. Heuristic algorithms are used to find the best parameters of prediction models, mainly metaheuristic optimization algorithms, such as the particle swarm optimization algorithm (PSO) [10],firefly algorithm (FA) [11], and genetic algorithm (GA) [12], to improve the predictive power of the model. The abovementioned literature uses a single model for prediction and has achieved good prediction results. Considering that the specifications and types of high-dimensional, small-sample production mode and the production equipment used is diverse, diverse types of information are collected. In this case, quality regression prediction is more difficult, and single machine learning is prone to overfitting. Therefore, the problems of low estimation accuracy and weak model robustness cannot be avoided. Regression problems usually have very complicated internal and external factors, which will affect the prediction performance of different machine-learning algorithms to varying degrees. In recent years, machine learning research based on ensemble learning has attracted the attention of the manufacturing industry and academia, and has become increasingly widely used in various fields, such as scientific research and data mining. The main task of ensemble learning is to train multiple learners on a dataset and then combine their respective predictions into the final result. [PERSON] et al. proposed a gaNet-C model based on the idea of integration model, which combined the excellent characteristics of a residual network model and a gradient enhancement model and had good convergence ability and regularization effect. It showed good predictive effect on both data [13]. The integrated method can take advantage of different models to obtain better prediction results. Ensemble learning has better generalization ability and stability, and different models with unique characteristics are combined to solve the limitations of a single model, thereby improving prediction performance [14].