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<p>Additive Manufacturing (AM) offers unprecedented design freedom, yet evaluating the manufacturing resources required for complex geometries remains a significant computational bottleneck during the early design phase. Traditional slicing software, while accurate, is too slow to support high-frequency iterative workflows such as Generative Design. To address this, we propose the Parameter-Aware Geometric Estimator (PAGE-Net), a Graph Neural Network (GNN) framework designed to instantly predict Life Cycle Inventory (LCI) data–specifically Part Mass, Support Mass, and Total Print Time–directly from raw 3D meshes. Unlike existing voxel-based deep learning methods that suffer from discretization errors or static parameter assumptions, PAGE-Net leverages Feature-Steered Graph Convolutions (FeaStConv) to extract topological features from the native mesh while dynamically incorporating user-defined printing parameters (e.g., infill density, layer height). Trained and validated on a comprehensive dataset of approximately 90,000 geometries using a robust 3-fold cross-validation scheme, the model achieves high predictive accuracy, with <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{R^2}\)</EquationSource> </InlineEquation> scores exceeding 0.96 for material and time estimation. Computational benchmarks demonstrate an average inference time of 77 milliseconds per object–offering a speedup of approximately <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{30\times }\)</EquationSource> </InlineEquation> compared to optimized command-line slicing. By providing near real-time feedback on manufacturing resources, this framework serves as a critical enabler for data-driven Eco-Design and automated topology optimization.</p>

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A parameter-aware graph neural network for estimation of manufacturing resources in additive manufacturing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

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  • Niccolò Giovenali,
  • Giulia Bruno,
  • Paolo Chiabert,
  • Frédéric Segonds

摘要

Additive Manufacturing (AM) offers unprecedented design freedom, yet evaluating the manufacturing resources required for complex geometries remains a significant computational bottleneck during the early design phase. Traditional slicing software, while accurate, is too slow to support high-frequency iterative workflows such as Generative Design. To address this, we propose the Parameter-Aware Geometric Estimator (PAGE-Net), a Graph Neural Network (GNN) framework designed to instantly predict Life Cycle Inventory (LCI) data–specifically Part Mass, Support Mass, and Total Print Time–directly from raw 3D meshes. Unlike existing voxel-based deep learning methods that suffer from discretization errors or static parameter assumptions, PAGE-Net leverages Feature-Steered Graph Convolutions (FeaStConv) to extract topological features from the native mesh while dynamically incorporating user-defined printing parameters (e.g., infill density, layer height). Trained and validated on a comprehensive dataset of approximately 90,000 geometries using a robust 3-fold cross-validation scheme, the model achieves high predictive accuracy, with \(\varvec{R^2}\) scores exceeding 0.96 for material and time estimation. Computational benchmarks demonstrate an average inference time of 77 milliseconds per object–offering a speedup of approximately \(\varvec{30\times }\) compared to optimized command-line slicing. By providing near real-time feedback on manufacturing resources, this framework serves as a critical enabler for data-driven Eco-Design and automated topology optimization.