Enhancing Latent Defect Detection in Built‐In Spindle Assembly Lines Through Vibration Data Analysis

This study proposed a novel machine learning–driven methodology for detecting potential defects in computer numerical control (CNC) spindle manufacturing. The methodology, which analyzes 13 real‐world built‐in spindles, employs t ‐distributed stochastic neighbor embedding ( t ‐SNE) for data visualization and enhances k ‐means++ clustering with the Davies–Bouldin Index (DBI) for the automatic selection of the optimal number of clusters, significantly surpassing traditional inspection methods in identifying subtle yet critical defects. This study utilized the fast Fourier transform (FFT) for precise feature extraction. The integration of these advanced algorithms accurately identified defects and categorized them, thus optimizing manufacturing processes. The inclusion of the DBI in the k ‐means++ clustering algorithm facilitated an objective evaluation of cluster quality, ensuring that the selected number of clusters accurately represents the underlying data patterns. This automated selection of the optimal k value enhanced the stability and reliability of the defect detection process. The proposed methodology substantially reduced the yield of defective spindles by identifying and addressing defects before spindle installation in CNC machines. The proactive defect detection and intervention system rectified potential failures at an early stage and improved the overall quality control processes. This proactive approach enhanced operational efficiency and reliability, reduced rework and warranty claims costs, and aligned with industrial needs while addressing a critical gap in academic research. This study significantly contributes to spindle manufacturing, ensuring high‐quality production outcomes and bridging important gaps in both industrial application and academic research.