Surface Roughness Measurements for Additive Manufacturing Slicing Algorithms

Experimental roughness data from three 3D printed models (ship, vase, dice) produced by a six-degree-of-freedom additive manufacturing device. Xingguo Han published this dataset on figshare in 2026 to compare the performance of different slicing algorithms. The 5.5 KB XLS file contains results from testing with a roughness meter.

Use Cases

• Compare surface roughness outcomes from equal-thickness, normal vector-based, and contour-based adaptive slicing algorithms for specific models like the ship, vase, and dice.
• Analyze the trade-off between printing accuracy and printing efficiency using the experimental roughness meter measurements.
• Validate new adaptive hierarchical slicing algorithms that use model contour information, normal vector angles, or Taubin vertex normal indices against this benchmark data.

Strengths

• Direct experimental measurements from a physical roughness meter on printed parts.
• Compares three distinct slicing algorithms (equal thickness, adaptive based on triangular facet normal, and the proposed contour-based method).
• Data supports a specific research claim about balancing printing accuracy and efficiency.

Limitations

• Very small dataset at 5.5 KB, likely containing only summary results from three test models.
• Unknown number of data rows, measurements per model, or specific roughness parameters recorded.
• Limited generalizability as results are tied to a specific six-DOF AM device and three model geometries.

Provenance

Source

Xingguo Han, published on figshare.

Collection Method

Experimental data from 3D printing models with a six-degree-of-freedom AM device and subsequent roughness meter testing.

Freshness

Dataset was last updated in 2026, indicating it is current or future-dated relative to typical archives.