A stochastic search algorithm incorporating geometric programming for total production-time minimisation in multi-pass face milling
International Journal of Production Research, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/00207543.2026.2749704
- Dergi Adı: International Journal of Production Research
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC, zbMATH, Biomedical Reference Collection: Corporate Edition (EBSCO), Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: cutting conditions optimisation, depth-of-cut allocation, geometric programming, Multi-pass face milling, population-based stochastic hill climbing
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
Multi-pass face milling is used when a prescribed stock allowance cannot be removed efficiently or safely in single-pass. The operation employs a multi-tooth face-milling cutter with its axis perpendicular to the machined surface. Successive passes remove layers with identical planar geometry and radial engagement but different axial depths. Selecting cutting conditions is challenging because the number of passes, depth of cut per pass, cutting speed, and feed rate must be determined while satisfying force, arbor-strength, deflection, machine capability, and power constraints. This study proposes a hybrid method for minimising total production time by combining population-based stochastic hill climbing (PBSHC) with geometric programming (GP). PBSHC searches feasible depth-of-cut allocations using an encoded vector and a decoder that enforces total depth, pass limits, depth bounds, and numerical precision. For each decoded plan, GP optimises cutting speeds and feed rates. Thus, stochastic search handles cutting-plan structure, while continuous variables are optimised mathematically. The method is implemented as an offline process-planning tool that outputs number of passes and corresponding depth of cut, cutting speed, and feed per tooth. Independent runs assess convergence, robustness, solution quality, and computational time. The results show that the method achieves the best feasible production time among published benchmarks.