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Impact of Fabrication Techniques and Polishing Procedures on Surface Roughness of Denture Base Resins

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Published on: January 17, 2025

Machine learning based approach for surface roughness prediction in precision dental prototyping.

Anmol Sharma¹, Ravinder S Saini², Ashish Kaushik³

¹USICT, Guru Gobind Singh Indraprastha University, Sector 16C, Dwarka, Delhi, India.

Scientific Reports

|September 1, 2025

Summary

This study optimized resin 3D printing for dental devices by developing a predictive model for surface roughness (SR). Ensemble machine learning, particularly XGBoost, significantly improved prediction accuracy, enhancing dental device quality.

Keywords:

Additive manufacturing Hyperparameter tuning Process modelling Resin

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Area of Science:

Additive Manufacturing
Materials Science
Machine Learning

Background:

Resin-based 3D printing enables complex geometries but often results in surface roughness, impacting dental device performance.
Surface roughness (SR) is a critical factor influencing the durability and effectiveness of 3D-printed dental devices.
Predictive modeling of SR is essential for optimizing 3D printing parameters in dentistry.

Purpose of the Study:

To develop and compare machine learning models for predicting surface roughness in resin 3D printing.
To identify optimal slicing parameters for minimizing surface roughness in dental applications.
To evaluate the performance of various machine learning algorithms, including ensemble methods, for this prediction task.

Main Methods:

Specimens were fabricated using a resin 3D printer with parameters determined by Design of Experiments (DoE).
Five factors (layer thickness, infill density, print angle, exposure time, lift speed) were investigated across 32 runs.
Artificial Neural Networks (ANN), Support-Vector Regression (SVR), Decision Trees (DT), Random Forest (RF), and XGBoost were employed and tuned.

Main Results:

Support-Vector Regression (SVR) showed strong performance with R² of 0.967 and RMSE of 0.018.
Ensemble methods outperformed base models, with XGBoost achieving the highest accuracy (R² = 0.998, RMSE = 0.003).
Hyperparameter tuning further enhanced model performance, validating the effectiveness of the chosen machine learning approaches.

Conclusions:

Ensemble machine learning models, especially XGBoost, provide highly accurate predictions for surface roughness in resin 3D printing.
This research offers a valuable tool for dental professionals to optimize 3D printing processes and improve the quality of dental devices.
The study highlights the potential of hybrid machine learning approaches in additive manufacturing for enhancing predictability and performance.