Data Augmentation and Deep Learning Approach for Cutting Force Monitoring in Milling Operation
Journal of Computing and Information Science in Engineering, cilt.26, sa.8, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26 Sayı: 8
- Basım Tarihi: 2026
- Doi Numarası: 10.1115/1.4072337
- Dergi Adı: Journal of Computing and Information Science in Engineering
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
- Anahtar Kelimeler: artificial intelligence, cutting-force monitoring, data augmentation, deep learning, Gramian angular field (GAF), manufacturing automation, smart manufacturing
- İstanbul Ticaret Üniversitesi Adresli: Evet
Özet
Data augmentation plays a critical role in improving the performance, generalization, and robustness of deep learning models, particularly when dealing with limited or imbalanced datasets. This study presents an approach to monitoring cutting forces in milling operations by integrating deep learning models with structured data augmentation. Time–frequency analysis methods are employed to extract dynamic signal characteristics, and the resulting representations are converted into image-based formats to enable the use of vision-based learning models. Multiple deep learning architectures are evaluated for classification and monitoring under varying cutting conditions. The results demonstrate that augmentation significantly enhances model robustness and performance. Among the evaluated models, the proposed convolutional neural network achieves the highest validation accuracy of 87.42% under specific augmentation settings, outperforming other architectures while maintaining low loss. These findings highlight the effectiveness of combining signal transformation, image-based representation, and augmentation strategies for reliable cutting-force monitoring.