A Stacked Neural Network Approach for Tool-Holder Health Classification Under Feature-Level Corruption

This paper presents a stacked neural network (StNN) framework for tool-holder health classification. The proposed architecture integrates a denoising autoencoder (DAE), used as a feature-reconstruction stage, with a multi-layer perceptron (MLP) classifier to distinguish between Healthy and Damaged tool-holder conditions. Vibration data were collected from Axial and Radial tool-holders tested under different rotational speeds and CNC machines. Six vibration descriptors—root mean square (RMS), average amplitude (AA), peak-to-peak (P2P), mean square frequency (MSF), gravity center frequency (GF), and mean spectrum amplitude (MSA)—were selected using training data only and combined with spindle speed and tool-holder type as classifier inputs. To avoid information leakage and pseudo-replication, model development and evaluation were performed using a single stratified group-wise hold-out split based on physical tool-holder units. The proposed StNN was compared with a direct MLP baseline, Random Forest, and SVM-RBF classifiers under clean/original and feature-level corrupted test conditions. On clean/original test data, the StNN achieved performance comparable to the direct MLP baseline, with balanced accuracy values of 0.8778 and 0.8694, respectively. Under feature-level corrupted test conditions, the StNN retained the highest balanced accuracy (0.8822), outperforming the direct MLP, Random Forest, and SVM-RBF baselines and showing essentially no degradation with respect to the clean/original condition. These results indicate that DAE-based feature reconstruction can preserve clean-data classification performance while improving robustness under the adopted feature-level corruption, supporting its use for feature-based tool-holder condition monitoring, with generalization assessed on unseen physical tool-holder units within a stratified group-wise hold-out split.