## 1 Introduction  
Currently, industrial real-time databases need AI data analysis technologies to solve the problems of defect detection [1], sorting identification [2], size detection [3], visual guidance [4], etc., which help enterprises to achieve flexible production [5] and high automation. For example, the intelligent defect detection makes the manual detection to be a laborious task as the human eye is not able to distinguish the fast moving tiny objects. Furthermore, the excessive use of eyes is much easier to miss objects. AI technologies can be used to overcome these difficulties and increase detection efficiency by means of adjusting the detection accuracy according to the product detection requirements. At the same time, AI technologies can realize automatic detection, automatic processing and reducing the rate of defective products and labor costs by means of cooperating with automatic production lines, which increase the production efficiency significantly [6]. However, the classical single AI algorithm applied in the current industrial production process is still insufficient. A single AI algorithm has different prediction capabilities for different data sets, and its adaptability to multiple production environments and multiple steel plates is also insufficient. It is also easier to fall into local optimization and cannot obtain the optimal solution. Aiming at the shortcomings of classical single AI algorithm, this project proposes an ensemble learning algorithm based on multi-machine learning, which effectively compensates for the shortcomings of a single AI algorithm and improves the prediction performance. As shown in Figure 1, certain internal stress generates in the manufacturing process of steel plates [7]. The magnitude of internal stress plays a key role in the application of steel products and the effect on final products. The internal stress of steel plates is unqualified, which may cause deformation and cracks of steel plates. Detecting unqualified steel plates and performing cold straightening is the key to improving the production quality of steel plates. The accuracy of whether steel plates need cold straightening cannot be guaranteed if it is only judged by workers' experience and naked eyes. Also, the effect is not ideal because it is usually affected by the status and quality of workers. These problems can be avoided by using AI technologies to make decisions on cold straightening of steel plates. The steel plate cold straightening auxiliary decision-making algorithm aims to predict whether the internal stress of steel products is qualified and cold straightening is necessary, which may lead to a more stable and accurate judgment on whether cold straightening is necessary in the steel plate production process. However, traditional single machine learning algorithms cannot adapt well to different types of steel plates (including steel plates of different sizes and different physical compositions, as their production environment and corresponding data can be very different) and production processes in actual production because they often fall into local optimum easily, and the adaptability of the model is limited [8]. Because a single kind of classical AI algorithm has different capabilities to process data sets with different characteristics, there is also a bias towards the data processing capabilities of different sizes and types of steel plates. Using the multi-classifier ensemble learning method can make up for the shortcomings of different classifiers and improve the final classification prediction effect. At present, it is an urgent and common problem in the steel production industry to propose a steel plate cold straightening auxiliary decision-making method with higher judgment accuracy and stronger model adaptability [9]. Ensemble learning [10] is a common statistical learning method, which is widely applied and effective. Ensemble learning usually achieves significantly better generalization performance than a single learner by combining multiple learners.