AM Dimensional Accuracy Estimator
Expected XY and Z dimensional deviation by process, material class, and geometry type. Based on aggregated published accuracy data (Moylan et al., EOS, ASTM F3303).
Process & Material
Geometry & Size
Geometry Class
Part Size Class
mm
Expected Dimensional Deviation
Based on aggregated published data. Values are for a single part on a calibrated, production-qualified machine.
XY (in-plane)
Mean bias+0.05 mm
1σ scatter±0.08 mm
Design tolerance (±2σ)±0.16 mm
Relative0.32% of nominal
Z (build direction)
Mean bias+0.10 mm
1σ scatter±0.12 mm
Design tolerance (±2σ)±0.24 mm
Relative0.48% of nominal
Verdict
Achievable IT grade:IT8-IT9
Post-machining:Not required
Higher confidence — values based on multiple published accuracy studies and manufacturer datasets.
Process Notes
- 01No process-specific warnings for this configuration. Verify against your machine calibration data.
Geometry Class Comparison
XY and Z design tolerance (±2σ) for each geometry class — current process and size class fixed.
| Geometry | XY bias | XY ±2σ | Z bias | Z ±2σ | IT grade |
|---|---|---|---|---|---|
| Bulkselected | +0.05 | ±0.16 mm | +0.10 | ±0.24 mm | IT8-IT9 |
| Thin-wall | +0.05 | ±0.26 mm | +0.10 | ±0.38 mm | IT8-IT9 |
| Tall | +0.05 | ±0.22 mm | +0.10 | ±0.34 mm | IT8-IT9 |
| Hollow | +0.05 | ±0.19 mm | +0.10 | ±0.29 mm | IT8-IT9 |
| Fine feature | +0.05 | ±0.29 mm | +0.10 | ±0.43 mm | IT8-IT9 |
Sources
Sources
- [1]Moylan S. et al. — Proposal for a standardized test artifact for additive manufacturing machines and processes — ASPE Proceedings, 2014
- [2]EOS GmbH — EOS M 290 Material Data Sheet (Ti64, dimensional accuracy) — EOS GmbH, 2023
- [3]ASTM F3303-18 — Standard for Additive Manufacturing: Practice for Metal PBF to Meet Critical Applications — ASTM International, 2018
- [4]DebRoy T. et al. — Additive manufacturing of metallic components: process, structure and properties — Progress in Materials Science, 2018
- [5]Mostafaei A. et al. — Binder jetting 3D printing: Process parameters, materials, properties, modelling, and challenges — Progress in Materials Science, 2021