Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function —provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems.

The problem of using the technique of pattern recognition and artificial intelligence to automaticly track lineups on section image is one of the important topics in automatic explanation of the seismic section data. This paper presents a new AR method of automatic tracing, which differs from the method given in [1] [2]. This new method uses AR model to represent the lineups for the first time and realizes AR auto-tracing bf lineups by a speolal data structure and a searching strategy. Compared with the results in [l] and [2], the method can mend up the fault spots to keep the continuity of lineups, offer the reference results for the recognition of the feathering-out and fault, and indicate the rough horizons divided by the lineups. The system-structure described in this paper also has interface which may import knowledge, hence ensure the posibility of realizing intelligent tracing.