Tool condition monitoring (TCM) is vital to maintain the quality of workpieces during machining. Recently, data-driven methods based on multisensory data have been applied to TCM. The quality of extracted features is a key to realizing a successful data-driven TCM. However, the extracted features in the previous study are focused on the multicollinearity of multisensory data, which is incapable of identifying the informative and discriminative information in the long time period aspect. This article proposed a novel method for TCM using deep spatial-temporal feature extraction and lightweight feature fusion techniques. A key to the proposed method is the extraction of multicollinearity as spatial features (SPs), and the capture of long-range dependencies and nonlinear dynamics as temporal features (TFs), to fully characterize tool wear change using multisensory data. Then, a lightweight feature fusion method is used to fuse SPs, TFs, and statistical features for further removing redundant information employing the kernel-principal component analysis. Finally, support vector machines is used to predict the tool conditions using the fusion feature. Experiments on a milling machine and a gear hobbing machine are carried out to verify the effectiveness and generalization of the proposed method respectively.

CoS₂NP/Al₂O₃NSs were developed as an efficient electrocatalyst for hydrogen evolution and as an anode for lithium ion batteries.

Most of domain adaptation (DA) methods just focus on the case that the label spaces of two domains are identical. However, it is more valuable for studying partial DA in practical engineering. Whereupon, a novel multiscale weight-selection adversarial network (MWSAN) is proposed to enhance the effect of partial DA. MWSAN is constructed via the designed multiscale domain adversarial network (MDAN) and the multiscale weight-selection mechanism of instance and class. MDAN is innovatively built to avoid the overfitting of a single classifier and strengthen the domain confusion. For the weight-selection of instance, the instance weights are directly obtained by the probability output of MDAN. For the weight-selection of class, the possible Gaussian distributions of target-domain samples is estimated by Gaussian mixture model, and the Gaussian distribution of each class of source-domain samples is computed by maximum likelihood estimation, then the Wasserstein distance is employed to computed the class weights. With the multiscale backbone network and weighting mechanism, MWSAN can achieve the partial DA to the larger degree. The proposed method is applied to the partial transfer fault diagnosis of planetary gearboxes with the unlabeled target-domain samples, and the experimental results indicate that MWSAN outperforms other typical DA methods.

This paper develops a proactive and reactive service composition (PRSC) method for CMfg based on digital thread. First, the digital thread-driven information interaction architecture is established, based on which the detailed process of PRSC and the constitution of digital thread are designed. Then, the strategy of proactive SC and the process of reactive service adjustment are proposed: the former selectively allocates alternative services for subtasks to balance the quality of service (QoS) and robustness of the composite manufacturing service, the latter implements the decision-making for service exception handling based on digital thread. Finally, a group of simulation experiments and a practical case study are conducted. The results show that the proposed digital thread-driven PRSC method integrates the advantages of proactive planning and reactive adjustment, effectively reduces the service reservation cost of robust service composition and improves the actual QoSs under uncertainties based on the support of near real-time information interaction.