Acquiring welding domain relationships and forming a knowledge graph can positively impact complex engineering problem solving and intelligent manufacturing applications. However, relationships are lacking in the welding domain. The relationship extraction and processing solution are designed to handle data with different characteristics in welding fabrication. The BiLSTM+Attention and CR-CNN models are employed to extract relations in unstructured documents. The neighborhood rough set-based association rule model is proposed for project-specific documents to accomplish relationship acquisition, in which invalid attributes are removed via neighborhood rough sets and attribute values are related via association rules. In addition, the knowledge graph is built based on extracted relationships, and unique empirical relationships are handled by introducing relational nodes and databases. The results show that BiLSTM+Attention gets a good score with Macro-average metrics (0.788 for Precision, 0.846 for Recall, and 0.816 for F1-score). The relational rules obtained via the proposed model are consistent with the production experience. The constructed knowledge graph effectively handles empirical relationships while positively impacting knowledge retrieval, intelligent question and answer, and decision-making for complex engineering problems.

The cold metal transfer (CMT) process is widely used in thin plate welding because of its characteristics of low heat input and stable arc. In actual production, a larger weld gap, misalignment, or other problems due to assembly error lead to serious welding defects, such as burn-through and a lack of fusion. The arc sound contains a wealth of information related to the quality of the weld. This work analyzes the mechanism of CMT arc sound generation, as well as the correlation between the time–frequency spectrum of the arc sound signal and welding quality. This paper studies the extraction of the multi-channel time–frequency spectrum of an arc sound and inputs it to a custom convolutional neural network for the CMT welding defect identification of thin aluminum alloy plates. The experimental result shows that the average accuracy of the proposed model is 91.49% in the defect identification of a CMT arc-welded aluminum alloy sheet, which is higher than that of the single-channel time–frequency convolutional neural network and other traditional classification models.

Knowledge acquisition and reasoning are essential in intelligent welding decisions. However, the challenges of unstructured knowledge acquisition and weak knowledge linkage across phases limit the development of welding intelligence, especially in the integration of domain information engineering. This paper proposes a cognitive model combining image recognition and a knowledge graph. A CNN is used as the perception layer to obtain direct information. Automated logic rules based on a knowledge graph are described to enable information integration in the knowledge reasoning domain. In addition, a welding knowledge graph of the bogie frame was constructed based on entity and relationship recognition. CNN models with different network structures were compared and trained under supervised conditions. In the results, the InceptionV1 network obtained a high score (0.758 for the thickness relation, 0.642 for the groove form, 0.704 for the joint type, and 0.835 for the base material form). The proposed model showed positive performance in terms of accuracy, interpretation, knowledge coverage, scalability, and portability compared with several other methods. The model can effectively address the abovementioned limitations and is important for welding manufacturing with engineering information integration.

The rapid development of welding fabrication demands guidance data to support expert decision-making, retroactive process, and production pattern upgrades. Yet, the data is lacking. This paper developed the guidance corpus for welding manufacturing. The unstructured document guiding actual production was collected, and the data were segmented into words employing a conditional random field model. In addition, we organized an annotation team and developed the tool to accomplish data annotation using a Begin-Inside-Outside method. Machine learning and deep learning models were trained for the named entity recognition task under supervised learning to accomplish the challenge of corpus scalability. The corpus contained 19,410 labeled pairs of samples and involved multiple entity categories, that is, “standard,”“technology,”“design,”“department,”“manufacturing,” and “quality.” The baseline of the model on Precision, Recall, and F₁-score metrics were listed to provide a reference for welding production research. Furthermore, we also gave some examples of task assignments and knowledge retrieval in conjunction with real production problems. The long-term goal was continually to enhance the corpus through more researchers, suggesting that a robust corpus would support more research and engineering applications in the welding domain.

The CMT welding process has been widely used for aluminum alloy welding. The weld’s penetration state is essential for evaluating the welding quality. Arc sound signals contain a wealth of information related to the penetration state of the weld. This paper studies the correlation between the frequency domain features of arc sound signals and the weld penetration state, as well as the correlation between Mel spectrograms, Gammatone spectrograms and Bark spectrograms and the weld penetration state. Arc sound features fused with multilingual spectrograms are constructed as inputs to a custom Inception CNN model that is optimized based on GoogleNet for CMT weld penetration state recognition. The experimental results show that the accuracy of the method proposed in this paper for identifying the fusion state of CMT welds in aluminum alloy plates is 97.7%, which is higher than the identification accuracy of a single spectrogram as the input. The recognition accuracy of the customized Inception CNN is improved by 0.93% over the recognition accuracy of GoogleNet. The customized Inception CNN also has high recognition results compared to AlexNet and ResNet.