Machining performance analysis and optimization in the milling of mold steel under MQL with nanofluid

Aydın M., Günay Y., Yapan Y. F., Livatyali H., Uysal A.

Machining Science and Technology, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1080/10910344.2025.2582201
  • Dergi Adı: Machining Science and Technology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Environment Index, INSPEC
  • Anahtar Kelimeler: Graphene nanofluid, Gray Wolf algorithm, minimum quantity lubrication, optimization, plastic mold steel
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

The presented study investigates the milling performance of DIN-1.2738 steel under various cutting speeds, feeds, dry, minimum quantity lubrication (MQL) and nanographene-reinforced nanofluid-assisted MQL (N-MQL) cutting conditions. The results of cutting temperature, cutting force, feed force and surface roughness were obtained using a full-factorial experimental design. Under the N-MQL cutting conditions, the cutting temperature, cutting force, feed force and surface roughness improved by 30.1%, 22.3%, 26.3% and 40.2%, respectively. The most effective parameters for cutting temperature, feed force and surface roughness turned out to be the cooling conditions, with 81.6%, 41.7% and 72% contribution ratios, respectively. Also, feed had the strongest effect on cutting force, with a 44.7% contribution ratio. Using different weight ratios, the Gray Wolf algorithm optimized the milling parameters and cooling conditions for output parameters. The optimization process used five scenarios, weight-prioritizing each output parameter and incorporating the entropy method. The optimum cutting condition and feed were 1% Graphene N-MQL and 0.04 mm/rev across all scenarios. The optimal cutting speeds varied based on different priorities.