VSG-FC: A Combined Virtual Sample Generation and Feature Construction Model for Effective Prediction of Surface Roughness in Polishing Processes

Surface roughness is a critical indicator for assessing the quality and characteristics of workpieces, the accurate prediction of which can significantly enhance production efficiency and product performance. Data-driven methods are efficient ways for predicting surface roughness in polishing processes, which generally depend on large-scale samples for model training. However, obtaining an adequate amount of training data during the polishing process can be challenging due to constraints related to cost and efficiency. To address this issue, a novel surface roughness prediction model, named VSG-FC, is proposed in this paper that integrates Genetic Algorithm-driven Virtual Sample Generation (GA-VSG) and Genetic Programming-driven Feature Construction (GP-FC) to overcome data scarcity. This approach optimizes the feature space through sample augmentation and feature reconstruction, thereby enhancing model performance. The VSG-FC method proposed in this paper has been validated using data from two polishing experiments. The results demonstrate that the method offers significant advantages in both the quality of the generated virtual samples and prediction accuracy. Additionally, the proposed model is explainable and could successfully identify key influencing machining factors.