Recent advances in artificial intelligence (AI) technology have led to increasing interest in the development of AI-based tool condition monitoring (TCM) methods. However, achieving good performance using these methods relies heavily on large training samples, which are both expensive and difficult to obtain in practical TCM applications. This article addresses this issue by employing a much smaller training sample composed of a non-exhaustive sampling of experimentally measured cutting force signals in conjunction with a novel data augmentation method that combines numerical simulation with a generative adversarial network (GAN). First, cutting force signal samples not present in the experimental dataset are obtained by numerical simulation using a finite element method simulated based on the Johnson-Cook model. Second, the GAN is employed to synthesize additional samples that are similar to both the simulated samples and the experimentally measured samples. The synthesized samples are combined with the measured and simulated samples to produce an appropriately large dataset necessary for the effective training of an AI classifier. The proposed sample augmentation method is applied in milling TCM experiments, and the classification accuracies obtained with several AI classifiers trained with the augmented dataset were all close to or equal to 100%.

Timely and effective identification and monitoring of tool wear is important for the milling process. However, traditional methods of tool wear estimation have run into difficulties due to under small samples with less prior knowledge. This article addresses this issue by employing a multisensor tool wear estimation method based on blind source separation technology. Stationary subspace analysis (SSA) technology is applied to transform multisensor signals to stationary and nonstationary sources without prior information of signals. Ten dimensionless time-frequency indices of the nonstationary signal are extracted to train least squares support vector regression (LS-SVR) to obtain a tool wear estimation model for small samples. The analysis and comparison of one benchmark tool wear dataset and tool wear experiments verify the feasibility and effectiveness of the proposed method and outperform other two current methods.

Transfer learning (TL) has been successfully implemented in tool condition monitoring (TCM) to address the lack of labeled data in real industrial scenarios. In current TL models, the domain offset in the joint distribution of input feature and output label still exists after the feature...

Pseudocapacitances are being increasingly explored due to their potential applications to achieve higher capacitance and energy density than the traditional electric double-layer capacitances. It is one of the urgent challenges to realize the simple, fast, and good process compatibility manufacturing of pseudocapacitance for its wide industrial applications. A one-step laser-induced plasma-assisted ablation-based deposition (LIPAA-based deposition) method for fabrication of embedded planar pseudocapacitive supercapacitors on the ITO glass surface was proposed. The surface morphology and structural characteristics of the designed pseudocapacitance interdigital electrode surfaces were investigated using the XRD, SEM, and XPS. The cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic charge-discharge (GCD) were used to analyze the electrochemical properties of the prepared glass-based planar pseudocapacitances, which achieved a potential range of − 0.5 to 0.4 V in the PVA/H₂SO₄ gel electrolyte due to the reaction induced by ions in the electrolyte intercalation/deintercalation process on the electrode surface. A maximum areal capacitance of 238.57 μF cm⁻² was achieved by the manufactured capacitor and ~ 80.022% initial capacitance retention after 2000 cycles. The one-step preparation of planar supercapacitors with ITO conductive glass surfaces as electrodes is of great significance for the commercial development of pseudocapacitors in the field of electrochemical energy storage.