Experimental Results for Adaptive Slicing Algorithms in Additive Manufacturing

Experimental results of dice, ship, and vase models printed using a six-degree-of-freedom additive manufacturing device. Data compares the performance of three slicing algorithms, including a proposed adaptive hierarchical method, with surface roughness measurements. Xingguo Han published this dataset on figshare in 2026.

Use Cases

• Compare surface roughness measurements from different slicing algorithms (equal-thickness, normal vector-based, adaptive hierarchical).
• Analyze the relationship between model part classification (non-significant, general, complex features) and resulting print quality metrics.
• Validate the proposed adaptive hierarchical algorithm's performance on specific test models (dice, ship, vase).

Strengths

• Experimental data includes surface roughness measurements from a roughness meter.
• Results compare three distinct slicing algorithms on three different 3D models.

Limitations

• Dataset is very small at 5.5 KB, indicating limited experimental scope.
• Specific row count, measurement details, and raw experimental parameters are unknown.

Provenance

Source

Xingguo Han via figshare.

Collection Method

Experimental results from models printed on a six-degree-of-freedom additive manufacturing device and tested with a roughness meter.

Freshness

Last updated in 2026.