FDM Process Parameters of AI-Generated 3D Models
Year: 2026
Type: preprint
Abstract: **Dataset Abstract** This dataset contains experimental records for fused deposition modeling (FDM) printing of representative AI-generated 3D models. The dataset links model-level geometric descriptors, FDM process parameters, and measured printing outcomes to support data-driven printability prediction and parameter optimization. Each 3D model was height-normalized before geometric feature extraction, and manufacturing-relevant features such as bounding dimensions, volume, surface area, aspect ratios, overhang-related descriptors, convexity, and fine-detail indicators were calculated from the mesh. The corresponding FDM process parameters include layer height, print speed, nozzle temperature, bed temperature, infill percentage, and support configuration. Printing outcomes include print feasibility, printing time, material consumption, and scan-based root-mean-square (RMS) geometric deviation. This dataset is intended to support research on geometry-aware surrogate modeling, multi-objective FDM process parameter optimization, and adaptive manufacturing of AI-generated 3D content.
Author: Sining Li
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7202276149