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The images of surface defects of industrial products contain not only the defect type but also the causal logic related to defective design and manufacturing. This information is recessive and unstructured and difficult to find and use, which cannot provide an apriori basis for solving the problem of product defects in design and manufacturing. Therefore, in this paper, we propose an image semantic refinement recognition method based on causal knowledge for product surface defects. Firstly, an improved ResNet was designed to improve the image classification effect. Then, the causal knowledge graph of surface defects was constructed and stored in Neo4j. Finally, a visualization platform for causal knowledge analysis was developed to realize the causal visualization of the defects in the causal knowledge graph driven by the output data of the network model. In addition, the method is validated by the surface defects dataset. The experimental results show that the average accuracy, recall, and precision of the improved ResNet are improved by 11%, 8.15%, and 8.3%, respectively. Through the application of the visualization platform, the cause results obtained are correct by related analysis and comparison, which can effectively represent the cause of aluminum profile surface defects, verifying the effectiveness of the method proposed in this paper.

The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.