Prediction of machine tool spindle assembly quality variation based on the stacking ensemble model

This paper addresses the challenges of traditional spindle assembly methods, which rely on trial-and-error approaches, hindering new product development evaluation. A stacking ensemble model is proposed to predict the assembly quality variation of machine tool spindles. The model uses data from 925 single-spindle inspections and extracts evaluation metrics from multiple domains to extract valuable information. Feature selection is performed using a correlation model to identify important features, and various lightweight supervised learning algorithms are applied to analyze the data. To further enhance the model’s performance, a stacking ensemble approach is proposed, which combines algorithms. The results demonstrate that the proposed stacking ensemble model is an effective approach for predicting the assembly quality variation of machine tool spindles, using the data available. The proposed ensemble model enhances quality control processes in spindle assembly, enabling practitioners to identify key features and predict machine tool spindle assembly quality variations more accurately.