Prediction of Machine Tool Spindle Assembly Quality Variation Based on the Stacking Ensemble Model

Abstract

This paper presents a stacking ensemble model 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 proposed ensemble model achieves an accuracy rate of 85.47%, a precision rate of 86.44%, a recall rate of 85.64%, and an F1 value of 86.04%. 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.