A robust mechanistic–metaheuristic hybrid approach for predicting milling force coefficients

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Bayram B. S., Atila Ü., Uzun G., Tekiner Z., Korkut İ., Yavuzyeğit B.

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS, PART E: JOURNAL OF PROCESS MECHANICAL ENGINEERING, cilt.0, sa.0, ss.1-15, 2025 (SCI-Expanded, Scopus)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 0 Sayı: 0
• Basım Tarihi: 2025
• Doi Numarası: 10.1177/09544089251390127
• Dergi Adı: PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS, PART E: JOURNAL OF PROCESS MECHANICAL ENGINEERING
• Derginin Tarandığı İndeksler: Applied Science & Technology Source, Scopus, Aerospace Database, Science Citation Index Expanded (SCI-EXPANDED), Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
• Sayfa Sayıları: ss.1-15
• Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
• Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

This study proposes an optimization-based methodology for predicting cutting forces in milling by eliminating the need for traditional offline calibration procedures. A mechanistic force model is employed, in which cutting force coefficients are identified using population-based metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Cutting force data collected during machining are utilized to optimize the model parameters directly. The performance of each algorithm is systematically evaluated through 30 independent trials to ensure statistical reliability. The DE algorithm demonstrated the best performance, converging in all 30 runs with an average of 197 iterations and 5.4 s, followed by PSO (363 iterations, 9.8 s), while GA exhibited lower reliability (18 successful runs, 2108 iterations, 62.9 s). The optimized coefficients were validated against experimental data, yielding mean prediction errors of 2.82 N (F_x) and 4.35 N (F_y). The proposed method offers a fast, accurate, and scalable solution for cutting force prediction, supporting adaptive process control, and contributing to the development of intelligent manufacturing systems.