Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
April 3, 2026International Journal of Material FormingOpen Access

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

View Full Paper
Ask AI
Bookmark
Share

Authors

AAAslan AkdulumYKYUNUS KAYIR

Discussion

Loading...

Member takes

Overview

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

Key Points

  • 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

Cite This Study

Akdulum et al. (2026) studied this question.

synapsesocial.com/papers/69cf5dd55a333a821460bd35https://doi.org/10.1007/s12289-026-02004-y
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Development of Surface Roughness Prediction and Monitoring System in Milling Process2024 · 13 citations
  2. 2AI-Driven Prediction of Surface Roughness and Cutting Force in Milling Aluminum Alloy Under Data-Scarce Conditions2026
  3. 3Advanced and explainable machine learning model for prediction of surface roughness of tempered steel AISI 10602026 · 2 citations
  4. 4AI-BASED MODELING AND ANALYSIS OF SURFACE ROUGHNESS, TOOL WEAR, AND MATERIAL REMOVAL RATE IN DRY HARD TURNING OF SKD11 FOR ADAPTIVE CONTROL INSIGHTS2026
  5. 5Predictive mapping of surface roughness in turning of hardened AISI 4340 using carbide tools2024