What question did this study set out to answer?

To develop an accurate model for predicting surface roughness in boring operations using limited data.

Prediction of surface roughness in boring of 1.2311 material using machine learning enhanced by virtual sampling methods

View Full Paper

Ask AI

Bookmark

Authors

Demonstrates improved surface roughness prediction in boring operations, highlighting the role of machine learning and augmented data methods.

To develop an accurate model for predicting surface roughness in boring operations using limited data.
Integrated machine learning methodology for surface roughness prediction
Utilized feature augmentation and principal component analysis (PCA)
Employed virtual sampling with interpolation techniques to expand datasets
Conducted experiments with limited data points (72)
Achieved minimum RMSE of 0.28 μm and MAPE of 12.42%
Improved prediction accuracy by 61.9% in MAPE and 34.88% in RMSE compared to traditional models
Weighted interpolation-based VSG significantly enhanced accuracy in surface roughness prediction

Akdulum et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

Demonstrates improved surface roughness prediction in boring operations, highlighting the role of machine learning and augmented data methods.

To develop an accurate model for predicting surface roughness in boring operations using limited data.
Integrated machine learning methodology for surface roughness prediction
Utilized feature augmentation and principal component analysis (PCA)
Employed virtual sampling with interpolation techniques to expand datasets
Conducted experiments with limited data points (72)
Achieved minimum RMSE of 0.28 μm and MAPE of 12.42%
Improved prediction accuracy by 61.9% in MAPE and 34.88% in RMSE compared to traditional models
Weighted interpolation-based VSG significantly enhanced accuracy in surface roughness prediction

Akdulum et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark