Machine learning models have been widely used in the field of cutting tool wear identification, achieving favorable results. However, in actual industrial scenarios, obtaining sufficient labeled samples is time consuming and costly, while unlabeled samples are abundant and easy to collect. This situation significantly affects the model’s performance. To address this challenge, a novel semi-supervised method, based on long short-term memory (LSTM) networks, is provided. The proposed method leverages both small labeled and abundant unlabeled data to improve tool wear identification performance. The proposed method trains an initial tool wear regression model using LSTM, using a small amount of labeled samples. It then uses manifold regularization to generate pseudo-labels for the unlabeled samples. These pseudo-labeled samples are combined with the original labeled samples to retrain the MR–LSTM model iteratively to improve its performance. This process continues until a termination condition is met. The method considers the correlation between sample labels and feature structures, as well as the correlation between global and local sample labels. Experiments involving milling tool wear identification demonstrate that the proposed method significantly outperforms support vector regression (SVR) and recurrent neural network (RNN)-based methods, when a small amount of labeled samples and abundant unlabeled samples are available. The average R² values in terms of the proposed method’s predicted results can reach above 0.95. The proposed method is a potential technique for low-cost tool wear identification, without the need to collect a large number of labeled samples.

Welding wire is a major type of welding consumable, which needs to be winded onto spools for sale. Currently, the winding process is accomplished manually due to obstacles such as automatic spool loading and clamping. When loading the spool, the angular position of the spool is a prerequisite for matching the drive rod on the spindle and drive bore on the spool. Therefore, this paper proposes a template-matching method combined with area-based matching and feature-point detection to measure the angular position of the spool, and presents a mechanical system that can rotate the spool to match the drive rod and push the spool onto the spindle. A novel feature-point distribution density (FPDD) method was developed to accelerate the matching process and improve matching reliability by pre-locating the searching area. The robustness and accuracy of the template-matching-based measuring method were validated using a built prototype of the mechanical system. The comparison result shows that the proposed method was superior in robustness, accuracy, and speed, and it was efficient for automatic spool loading in the welding wire winding process.

Alumina (Al₂O₃) composite ceramics with different composition ratio and particle-size distribution were fabricated by the material extrusion and photo-polymerization combined process (MEX-PPM) based on additive-manufacturing (AM) technology in our previous work. These particles were nanosized Al₂O₃ (n-Al₂O₃), micron-sized TiCN (μ-TiCN) and Al₂O₃. Effects of n-Al₂O₃ and μ-TiCN on Al₂O₃ composite ceramics were investigated by characterizing the volume density, EDS spectrum, mechanical properties and microstructure of the prepared samples. It was found that n-Al₂O₃ had a significant effect on the hardness of Al₂O₃ composite ceramics, μ-TiCN, with excellent performance in density, flexural strength and fracture toughness. The Al₂O₃ composite ceramics with optimum contents of 10 wt % n-Al₂O₃ and 30 wt % μ-TiCN showed good microstructure and mechanical properties. Their porosity and volume density were at 4.073% and 4.177 g/cm³, respectively. Their hardness, flexural strength and fracture toughness were at 16.592 GPa, 592.875 MPa and 6.308 MPa/mm². The flexural strength of the ceramics was significantly higher than that of Al₂O₃ ceramics prepared by SLA in document (178.84 ± 17.66 MPa), which had great potential in high-pressure strength structure.

To reduce the experimental costs associated with tool condition monitoring (TCM) under new cutting conditions, a finite element modeling (FEM)-assisted deep subdomain adaptive network (DSAN) approach is proposed. Initially, an FEM technique is employed to construct a cutting tool model for the new cutting condition (target domain), and the similarity between simulated and experimental data is assessed to obtain valid simulated samples for the target domain. Subsequently, the time–frequency Markov representation method is utilized to extract imaging features from the simulated samples, which serve as input features for the monitoring model. Then, a DSAN model is established to facilitate the transfer from simulation to reality, with the source domain comprising a simulated sample set under new cutting conditions that includes various types of tool conditions obtained through FEM, and the target domain containing only a limited number of normal tool condition samples under new cutting conditions. The application analysis has demonstrated the effectiveness of the proposed method, achieving a classification accuracy of 99%. The proposed approach can significantly reduce experimental costs and obtain high-precision diagnostics of tool conditions with a small sample size.

With the rapid development of the electronic information industry, more and more attention has been paid to piezoelectric ceramic materials, but the electrical properties and characteristics of piezoelectric ceramic materials have problems such as high cost and inconvenient measurement. In this paper, a new method of electroinduced strain measurement of piezoelectric ceramics is proposed, and an innovative measuring device is constructed based on the working mode and testing principle of an optical displacement sensor and piezoelectric ceramics. An optical displacement measuring device with a simple structure, convenient operation, high measurement accuracy, and good cost benefit was designed and manufactured, and the electroinduced strain performance of piezoelectric ceramics was effectively measured. It is verified by experimental analysis that the device can accurately measure the axial displacement of various piezoelectric ceramics, the measurement accuracy is comparable to the existing equipment, the error is less than 10%, and has good stability and repeatability, which provides a reliable technical means for the performance measurement of piezoelectric ceramics.

The sintering process can improve the microstructure of Al₂O₃ composite ceramics and enhance their comprehensive properties, but the effects of the sintering process on Al₂O₃ composite ceramics are still unclear. Herein, a novel Al₂O₃ composite ceramic was printed using the material extrusion and photo-polymerization combined process, and the final ceramic was obtained using one-step sintering (TS) and two-step sintering technology (TSS). Based on the testing results, such as the relative density (D_rel), average grain size (AGS), hardness, bending strength, and fracture toughness, TSS was suitable for the refinement of commercial Al₂O₃ ceramics. Moreover, the highest sintering temperature of the second step (T₂) was at 1550 °C, while that of the shortest holding time (t) was at 4 h (TSS₈), which was to ensure densification before rapid grain growth. The D_rel and AGS of the best ceramics obtained via TSS₈ were 97.65% and 1.52 µm, respectively. Their hardness, bending strength, and fracture toughness were also enhanced, and they were affected by T₂, t, and the interaction. In sum, the TSS obtained better fracture toughness and bending strength, which had great potential in the application of the additive manufacturing field.