## 1 Introduction  
Strip steel [1], a critical material in the industry, is employed extensively across diverse fields due to its notable mechanical properties and high surface quality. Nevertheless, during the manufacturing process, various defects may appear on strip steel, including scratches, pits, and inclusions. Such defects not only affect the aesthetic quality of the product but may also compromise its mechanical performance. Consequently, the prediction and identification of strip steel defects are of great significance for the improvement of product quality and production efficiency. The traditional methods for detecting defects in strip steel, which often rely on manual inspection and simple automation techniques, are beset with challenges. These include high labour costs, subjectivity in defect assessment, and limited detection capabilities. In recent years, machine learning techniques have proven to be highly effective in defect detection, offering a solution for automating the process of strip steel defect detection. Among the various machine learning techniques, stacking [2] is an ensemble learning method that combines multiple base models into a meta-model in order to leverage the strengths of each individual model for accurate predictions. This method has been employed in a variety of industrial domains, including the prediction of the shear strength of reinforced concrete beams [3], the lifetime prediction of electronic devices [4], and the identification of inferior compositions in steel [5]. Nevertheless, the issue of data imbalance [6] has a negative impact on the application of machine learning to defect prediction. This imbalance can result in a biased learning process, leading to models that perform well on the majority class but poorly on the minority class. To address this issue, the ISmote algorithm is employed to achieve data balance. Moreover, it is of paramount importance to optimise the hyperparameters of machine learning models in order to enhance predictive performance [7]. Conventional hyperparameter optimisation techniques, such as grid search and random search, frequently prove inadequate for efficiently navigating the intricate and high-dimensional hyperparameter space associated with stacking models. To address this issue, the IIPSO algorithm is employed to identify optimal hyperparameter settings for the model. This paper presents a comprehensive model framework integrating the ISmote algorithm, the stacking model, and the IIPSO algorithm. The objective of this framework is to enhance the accuracy of defect prediction, thereby contributing to the advancement of quality control processes in the strip steel industry. The primary contributions of this paper are as follows:  
1. At the stage of data processing, the K-means++ algorithm is employed to cluster the minority classes. Subsequently, the number of samples generated in each cluster is determined based on the density of each cluster. Based on the determined synthetic samples, the Smote algorithm is used to synthesise samples in the minority cluster. The ISmote algorithm can assist the model in avoiding bias towards the majority class, thereby enhancing its classification performance. 2. The standard IPSO algorithm is prone to premature convergence. To address this challenge, IPSO is enhanced with a dynamic parameter adjustment mechanism and a mutation strategy. During the model training phase, the IIPSO algorithm is used to optimise the hyperparameters of the stacked model, thereby obtaining more accurate defect prediction results. 3. The experimental evaluations were conducted on the strip defect dataset. The results demonstrate that the predictive model, enhanced by data balance and hyperparameter optimization, significantly outperforms all comparator models in terms of defect prediction. The rest of this paper is structured as follows. Section 2 provides a concise overview of related work on the processing of class imbalance and the optimisation of model hyperparameters.