Experimental Results for Adaptive 3D Printing Slicing Algorithms

Experimental data supports a new adaptive hierarchical slicing algorithm for fused deposition additive manufacturing. The 16.7 KB dataset contains results from slicing ship, vase, and dice STL models and measuring printed surface roughness. Xingguo Han published the data on figshare in April 2026.

Use Cases

• Compare surface roughness measurements from prints using equal thickness, normal vector-based, and the proposed adaptive slicing algorithm.
• Analyze the relationship between model part classification (non-significant, general, complex feature) and chosen slicing criteria.
• Validate the algorithm's performance on specific test models like the ship, vase, and dice STL files mentioned in the description.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• Experimental results include comparisons across three distinct slicing algorithms.

Limitations

• Dataset size is only 16.7 KB, indicating very limited experimental scope.
• Unknown row count and specific column names limit detailed analysis.

Provenance

Source

Xingguo Han via figshare.

Collection Method

Experimental results from slicing STL models and measuring printed outputs with a roughness meter.

Freshness

Last updated in April 2026.