Assembly process designers typically confront the challenge of seeking information out of large volumes of non-structured files with a view to supporting the decision-making to be made. It is a leading concern that embedding data in text documents can hardly be retrieved semantically in order to facilitate decision-making with timely support. For tackling this gap, we propose in this paper a knowledge graph-based approach used to merge and retrieve information decided to be relevant within an engineering context. The proposed approach is to establish a multidimensional integrated assembly resource knowledge graph (ARKG) based on the structure of function-structure-assembly procedure-assembly resource, and this multidimensional integrated structure can well accomplish the retrieval of related knowledge. The upper semantic framework of ARKG is established by the assembly resource ontology model, which is a semantic-type framework involving multiple domains of knowledge to create instantiated data reflecting the full profile of the assembly resource for obtaining structured data of ARKG while avoiding the data redundancy problem. The ARKG method is validated through assembly scenario of the aircraft, and the results show the effectiveness and accuracy of the ARKG used by the assembly process designer in the assembly process design phase for retrieving the target knowledge of the assembly resources.

Under the paradigm of intelligent manufacturing, web-based intelligent CAPP will become the trend of machining process planning. Machining process knowledge base is the foundation for implementing web-based intelligent CAPP. Massive historical machining process planning data accumulated in different sources become the main carrier of machining process knowledge. Hence, web-based intelligent CAPP puts forward the demand for data-driven construction of machining process knowledge base. Heterogeneous knowledge and unstructured data pose challenges for data-driven construction of machining process knowledge base. This article proposes a data-driven method combining knowledge graph with deep learning for constructing machining process knowledge base to provide more effective support for web-based intelligent CAPP. Firstly, a unified ontology model of machining process knowledge which integrates multi-domain machining process knowledge at the fine-grained semantic level is proposed, providing a unified semantic structure for the acquisition and organization of machining process knowledge based on distributed and unstructured process data. Secondly, a deep learning model consisting of Bert, Bi-LSTM, and CRF models deployed within the federated learning framework is proposed to automatically extract structured machining process knowledge from unstructured process texts with improved performance. Eventually, the prototype system for machining process knowledge graph construction can verify the effectiveness of the proposed method.

In this paper, the Dropout deep convolutional neural network based on ecg signal anomaly detection method is proposed. Then, the double threshold method is used to detect the R wave and calculate the width of corresponding QRS wave group and the position of QRS wave is taken as the reference to include several points forward and backward respectively. These data points are intercepted and made into data sets for training and testing of deep convolutional neural network. Finally, the data set is read into deep convolutional neural network for abnormal measurement, and the test results are statistically analysed. In this paper, the detection rate of QRS composite wave is up to 99.5%, and the classification accuracy of abnormal signals can reach 99.26%. Experimental results show that the algorithm has the characteristics of high implementation rate, high accuracy, convenient and fast.

While deep learning has significantly improved the accuracy of visual detection systems, the integration of process semantics with visual perception for industrial assembly tasks remains largely unexplored. This study aims to develop a high-accuracy cable connector detection framework that incorporates process knowledge to enhance feature discrimination under varying industrial imaging conditions. To achieve this goal, we build an enhanced Fully Convolutional One-Stage (FCOS) detector with a ConvNeXt V2 backbone and introduce a Process Feature Linear Modulation (PFNM) module. The proposed module adaptively modulates visual features using encoded process semantics, enabling the detector to align visual perception with assembly logic. Experiments conducted on an industrial connector dataset demonstrate that the proposed method achieves an mAP of 84.7%, outperforming representative state-of-the-art detectors including YOLOv11, RT-DETR, and DINO while maintaining an inference speed of 17.6 FPS. Ablation studies further show that each component contributes to progressive performance improvement, and the complete framework achieves a 5.5% AP gain over the ResNet-50 baseline. These results indicate that integrating process knowledge with visual feature learning effectively improves feature discrimination and provides a promising paradigm for process-aware perception in intelligent manufacturing.