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Automated Feature Recognition in Surface Cad Models Based on Graph Neural Networks

  • Driven by increasing customer demands, manufacturing processes encompass increasingly intricate workflows. The industry relies on computer-aided process planning software to effectively manage the intricacies of these complex manufacturing processes. This software is crucial to analyze computer-aided design data for a product and determining the required machining steps. However, a notable challenge arises, particularly in the case of custom products, where the machining steps can significantly vary depending on the available machinery and the employees' preferences.This study introduces a configurable automated feature recognition framework based on expert knowledge. Experts can encode their insights and expertise within this framework using a configurable synthetic data generator. Modern graph neural networks learn from the data generated by this data generator, achieving an average recognition accuracy up to 80% (F1-score) with a runtime performance of 21.98 milliseconds per model. Importantly, it accomplishes this even when confronted with highly intersecting machining features without requiring data conversion into alternative formats, such as voxel or pixel representations.

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Metadaten
Author:Stefan-Andreas Böhm, Bare Luka Zagar, Fabian Riß, Christian Kortüm, Alois Christian Knoll
DOI:https://doi.org/10.2139/ssrn.4772779
Document Type:Preprint
Language:English
Publication Year:2024
Tag:3D deep learning; CAD; Graph neural networks; Machining feature recognition; Snthetic data generation; Surface models