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Three-dimensional (3D) shape accuracy is a critical performance measure for products built via AM. With advances in computing and increased accessibility of AM product data, machine learning for AM (ML4AM) has become a viable strategy for enhancing AM. Proper description of the 3D shape formation through the layer-by-layer fabrication process is critical to incorporate process domain knowledge into ML4AM. The physics-based modeling and simulation approaches present voxel-level description of an object formation from points to lines, lines to surfaces, and surfaces to 3D shapes. However, this computationally intensive modeling framework does not provide a clear structure for machine learning of AM data. Chapter 5 introduces domain-informed small-sample learning and prediction of shape accuracy of 3D objects.

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Small-Sample Learning and Prediction of 3D Geometric Quality

  • 章节

  • 发表日期：

    2025年

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  • Qiang Huang

摘要

Three-dimensional (3D) shape accuracy is a critical performance measure for products built via AM. With advances in computing and increased accessibility of AM product data, machine learning for AM (ML4AM) has become a viable strategy for enhancing AM. Proper description of the 3D shape formation through the layer-by-layer fabrication process is critical to incorporate process domain knowledge into ML4AM. The physics-based modeling and simulation approaches present voxel-level description of an object formation from points to lines, lines to surfaces, and surfaces to 3D shapes. However, this computationally intensive modeling framework does not provide a clear structure for machine learning of AM data. Chapter 5 introduces domain-informed small-sample learning and prediction of shape accuracy of 3D objects.