Effect of cutting parameters on the machinability of X37CrMoV5-1 hot work tool steel


ÖZDEMİR M.

Materialpruefung/Materials Testing, cilt.64, sa.3, ss.412-429, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 64 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1515/mt-2021-2029
  • Dergi Adı: Materialpruefung/Materials Testing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.412-429
  • Anahtar Kelimeler: ANOVA, CNC turning, cutting parameters, machinability, X37CrMoV5-1 steel, SURFACE-ROUGHNESS, DESIGN OPTIMIZATION, POWER-CONSUMPTION, PREDICTION MODEL, ALLOY-STEEL, FEED RATE, FORCES, WEAR, ENERGY, EFFICIENCY
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

© 2022 Walter de Gruyter GmbH, Berlin/Boston.Hard turning was carried out on an X37CrMoV5-1 hot work tool steel with a hardness of 50 ± 2 HRC on a computer numerical control lathe, using a ceramic insert without the use of a coolant. The cutting parameters included three different cutting speeds, three different feed rates, and three different cutting depths. A full factorial design (FFD) was created, and 33=27 experiments were carried out. The effects of cutting parameters on cutting force (Fc), surface roughness (Ra), material removal rate (MRR), specific cutting energy (SCE), current (Cu), and sound intensity (SI) were investigated. As a result of the analysis of variance (ANOVA), the effect ratios of cutting parameters on Fc, Ra, MRR, SCE, Cu, and SI were examined, and important parameters were determined. As a result, the effective rates of the feed rate, which is the most effective parameter, on Fc, Ra, and MRR were determined as 61.72, 95.90, and 61.70%, respectively. The cutting depth was 54.81 and 34.37% on SCE and SI, respectively, and the cutting speed was effective on Cu by 79.87%. By using FFD and response surface methodology (RSM), the regression equations of the results of Fc, Ra, MRR, SCE, Cu, and SI were extracted, and r 2 values were examined. In the validation experiments performed after the optimization experiments, the experimental results were estimated using FFD, RSM, and Taguchi method, and the differences between them were analyzed.