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.

In the field of industrial safety, due to the existence of color, distance and other reasons in complex industrial environments caused by the helmet small target detection methods have the problem of misdetection and omission, and the Yolov5s model for real-time detection of helmets is not ideal. for the purpose of Improvement of extraction capabilities multiscale features, improve the network's focus on key features, and achieve an excellent balanced effect for accuracy and speed, the model Yolov5s-PSG based on Yolov5s improvement is proposed. First, the C3_PPA module improves the detection accuracy through a multi-branch feature extraction strategy and an attention mechanism. Second, the SimAM attention mechanism enhances the inspection accuracy by adaptively weighting the feature maps and thus improving the detection efficiency. Finally, the GSconv convolutional layer combines the SC module and the DSC module in a channel shuffling manner to infiltrate the information of the SC module within some of the information of the DSC module, which improves the detection speed without changing the accuracy.Yolov5s-PSG model outperforms the conventional target inspection in terms of accuracy, recall, map value, and loss function, where the accuracy is improved to 93.1%, recall to 91.2%, and map value to 94.6%. These findings are important for helmet inspection to find the optimal solution in terms of accuracy and speed for better helmet inspection in shop-floor factory environments.

Assembly precision analysis (APA) plays an important role in the whole life cycle of complex products design, manufacturing, assembly and even remanufacturing. Assembly precision information model (APIM) is usually complex since it is affected by many factors, such as design tolerance of parts, assembly process scheme, assembly sequence planning and tolerance of positioning tooling, etc. Therefore, it is of practical significance for APA to reasonably reduce the workload of assembly precision information (API) modeling. A semantic simplification approach for APIM is proposed in this paper, which mainly takes semantic relations between APIM and design tolerance of parts into consideration. Initially, ontology of structure knowledge of APIM is constructed according to a tolerance standard. Furthermore, simplification rules are respectively established by considering two semantic relations: one semantic relation between deviation change direction and deviation accumulation direction and the other semantic relation among multiple geometric characteristics on the same geometric feature. Additionally, by utilizing ontology reasoning function, the simplified semantic APIM is generated. Finally, the effectiveness of the proposed method is demonstrated by a practical example of engine front auxiliary drive equipment. It is expected that our work would lay the foundation for APA of complex products based on actual measured data.

Micromachining techniques have been applied widely to many industrial sectors, including aerospace, automotive, and precision instruments. However, due to their high-precision machining requirements, and the knowledge-intensive characteristics of miniaturized parts, complex manufacturing process problems often hinder production. To solve these problems, a systematic scheme for structured micromachining process problem solving and an innovation support system is required. This paper presents a knowledge-based holistic framework that enables process planners to achieve micromachining innovation design. By analyzing innovation design procedures and available knowledge sources, an open multi-source Machining Process Innovation Knowledge (MPIK) acquisition paradigm is presented, including knowledge units and a knowledge network. Further, a MPIK network-driven structured process problem-solving and heuristic innovation design method was explored. Subsequently, a knowledge-driven heuristic design system for machining process innovation was integrated in the Computer-Aided Process Innovation (CAPI) platform. Finally, a case study involving specific process problem-solving and innovation scheme design for micro-turbine machining was studied to validate the proposed approach.