Small data set analysis in surface metrology: an investigation using a single point incremental forming case study

Area of Science:

• Surface metrology
• Statistical analysis
• Materials science

Background:

• Surface metrology often requires large datasets for statistical analysis.
• Previous studies on single point incremental forming (SPIF) lacked robust statistical validation.
• Differentiating surface characteristics and manufacturing processes statistically is challenging with limited data.

Purpose of the Study:

• To demonstrate a novel statistical method for analyzing small datasets in surface metrology.
• To differentiate between surfaces or surface-creation processes using minimal measurement regions.
• To quantitatively assess the impact of surface topography on SPIF.

Main Methods:

• Application of advanced statistical techniques tailored for small sample sizes.
• Utilizing as few as six measurement regions for surface differentiation.
• Case study focusing on surface roughness analysis in single point incremental forming.

Main Results:

• The developed method successfully differentiated surface characteristics.
• Surface roughness parameters Sz and relative length (at scales < 200 nm) were significantly higher.
• These higher roughness values were observed when surface roll marks were oriented perpendicular to the forming direction compared to parallel.

Conclusions:

• The new statistical approach is effective for analyzing small datasets in surface metrology.
• Surface topography, specifically roll mark orientation, significantly influences surface roughness in SPIF.
• Findings provide quantitative insights into SPIF process optimization and surface quality control.