date-created: '2024-03-28'
date-updated: '2025-03-06'
manifest-author:
    name: Appropedia
    email: admin@appropedia.org
title: 'Synthetic-to-real Composite Semantic Segmentation in Additive Manufacturing'
description: 'The application of computer vision and machine learning methods for semantic segmentation of the structural elements of 3D-printed products in the field of additive manufacturing (AM) can improve real-time failure analysis systems and potentially reduce the number of defects by providing additional tools for in situ corrections. This work demonstrates the possibilities of using physics-based rendering for labeled image dataset generation, as well as image-to-image style transfer capabilities to improve the accuracy of real image segmentation for AM systems. Multi-class semantic segmentation experiments were carried out based on the U-Net model and the cycle generative adversarial network. The test results demonstrated the capacity of this method to detect such structural elements of 3D-printed parts as a top (last printed) layer, infill, shell, and support. A basis for further segmentation system enhancement by utilizing image-to-image style transfer and domain adaptation technologies was also considered.'
keywords: '[[3D printing]], [[additive manufacturing]], g-code segmentation, sim-to-real, semantic segmentation, synthetic data, machine learning, open source software, [[open-source hardware]], [[RepRap]], computer vision, quality assurance, real-time monitoring, anomaly detection; Blender, synthetic images'
project-link: 'https://www.appropedia.org/Synthetic-to-real_Composite_Semantic_Segmentation_in_Additive_Manufacturing'
contact:
    name: U
    social:
        platform: Appropedia
        user-handle: U
version: 6
development-stage: D
made: false
variant-of:
    title: '3'
    web: 'https://www.appropedia.org/3'
license:
    documentation: C
licensor:
    name: U
    affliation: C
    contact: 'https://www.appropedia.org/U'
documentation-home: 'https://www.appropedia.org/Synthetic-to-real_Composite_Semantic_Segmentation_in_Additive_Manufacturing'
documentation-language: e