Experimental Slicing Algorithm Results for Ship and Vase Models

Experimental results compare three slicing algorithms for additive manufacturing, applied to a ship model, vase model, and dice model. The dataset contains measurements, likely surface roughness, from printed models tested with a roughness meter. Xingguo Han published the data on figshare in April 2026.

Use Cases

• Compare printing accuracy metrics, such as surface roughness, between equal-thickness, normal vector-based, and contour-based adaptive slicing algorithms.
• Analyze the relationship between model part classification (non-significant, general, or complex feature) and resulting print quality measurements.
• Validate the proposed algorithm's performance on specific test geometries like the ship, vase, and dice models documented in the study.

Strengths

• Data is directly linked to a described experimental methodology using a six-DOF additive manufacturing device and a roughness meter.
• Published under a permissive CC-BY-4.0 license, allowing for reuse and redistribution.

Limitations

• The dataset is extremely small at 5.5 KB, indicating very limited sample size and scope.
• Specific column names, row counts, and the exact structure of the experimental measurements are not provided.

Provenance

Source

figshare, authored by Xingguo Han.

Collection Method

Experimental results from 3D printing tests comparing slicing algorithms.

Freshness

Last updated in April 2026, indicating recent publication.