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Processing feature recognition based on improved YOLOv5 algorithm
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AI
DOI：10.1007/s00170-025-16266-4.png)
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Abstract
En 中文
Processing feature recognition technology can effectively integrate CAD and CAPP systems, thus realizing the effective interoperability of design information and manufacturing information. Classical processing feature recognition technology does not have the ability to learn, recognition efficiency is low, and the accuracy of the recognition results cannot be guaranteed, while the deep neural network technology not only has the ability to learn, and noise resistance can improve the accuracy of the recognition; therefore, the proposed deep learning-based processing feature classification and recognition technology, the use of multi-angle downscaling to capture the processing features of the processing features of the image method to create a processing feature dataset and the deep neural network model through the YOLOv5 improved deep neural network model to train the dataset, and use the machining feature classifier to classify the processed part model and output the machining feature results. The machining feature recognition process is verified with typical parts as an example by combining with UG platform, which provides a new idea for machining feature recognition technology.
Keywords:
Processing characteristics
Image recognition
Deep neural network
Data set
Journal
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