Experimental Results for Adaptive 3D Printing Slicing Algorithms

Experimental data compares three slicing algorithms for fused deposition additive manufacturing. The dataset contains results from printing and testing ship, vase, and dice models on a six-degree-of-freedom AM device. Xingguo Han published this data on figshare in April 2026.

Use Cases

• Compare surface roughness measurements from the equal thickness, normal vector-based, and proposed adaptive slicing algorithms.
• Analyze the relationship between model part classification (non-significant, general, complex feature) and printing accuracy metrics.
• Validate the proposed algorithm's performance on specific test models (ship, vase, dice) using experimental print data.
• Benchmark printing efficiency against printing accuracy using results from the six-DOF AM device tests.

Strengths

• Data is derived from controlled experiments on a six-degree-of-freedom additive manufacturing device.
• Explicitly compares three distinct slicing algorithms on three different 3D models.
• Includes physical surface roughness measurements from a roughness meter.

Limitations

• Dataset is very small at 5.5 KB, indicating limited scope and likely a small number of experimental runs.
• Specific sample size, row count, and measured variables (columns) are not provided.
• The temporal and geographic context of the experiments is unspecified.

Provenance

Source

Xingguo Han via figshare.

Collection Method

Data generated from experimental 3D printing and surface roughness testing.

Freshness

Last updated in April 2026.