# A Taguchi-Based and Data-Driven Assessment of Surface Roughness and Wettability in FDM-Printed Polymers

**Authors:** Mehmet Albaşkara, Eyyup Gerçekcioğlu

PMC · DOI: 10.3390/mi17030322 · Micromachines · 2026-03-05

## TL;DR

This study examines how FDM printing parameters affect surface roughness and wettability in polymers, using a Taguchi method and neural networks to predict outcomes.

## Contribution

A novel Taguchi-based and data-driven approach to assess surface properties in FDM-printed polymers.

## Key findings

- Surface roughness is mainly influenced by layer thickness.
- Wettability is strongly affected by printing orientation due to surface anisotropy.
- ANN models effectively predict surface roughness and contact angle trends.

## Abstract

Fused Deposition Modeling (FDM) enables rapid, flexible production of polymer-based parts; however, because of additive manufacturing’s nature, it creates distinct microscale surface structures. These micro-scale surface morphologies directly affect the functional properties of the parts, such as surface roughness and wettability. In this study, the surface roughness and contact angle behavior of PLA, PETG, and ABS samples printed via FDM were investigated by varying layer thickness, print orientation, and infill density. The experimental design was created using a Taguchi L16 orthogonal array. Surface roughness was determined by optical profilometry, and wettability was measured by static contact angle tests. Surface topography was supported by scanning electron microscopy (SEM) and three-dimensional surface analyses. The findings revealed that surface roughness is predominantly dependent on layer thickness, whereas wettability is more strongly influenced by printing orientation, which determines the surface’s anisotropy. The developed artificial neural network (ANN) models successfully predicted the trends in surface roughness and contact angle outputs. This study reveals the effect of micro-scale surface structures formed in the FDM process on functional surface behavior, offering a fundamental framework for developing designable surfaces for micromechanical, microfluidic, and biomedical applications.

## Full-text entities

- **Chemicals:** PLA (MESH:C033616), polymer (MESH:D011108), FDM-Printed Polymers (-), PETG (MESH:C066907)

## Full text

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## Figures

12 figures with captions in the complete paper: https://tomesphere.com/paper/PMC13028808/full.md

## References

44 references — full list in the complete paper: https://tomesphere.com/paper/PMC13028808/full.md

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Source: https://tomesphere.com/paper/PMC13028808