Influence of graphene nanofluid on various environmental factors during turning of M42 steel


Anandan V., Babu M. N., Sezhian M. V., YILDIRIM Ç. V., Babu M. D.

JOURNAL OF MANUFACTURING PROCESSES, cilt.68, ss.90-103, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 68
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.jmapro.2021.07.019
  • Dergi Adı: JOURNAL OF MANUFACTURING PROCESSES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC
  • Sayfa Sayıları: ss.90-103
  • Anahtar Kelimeler: Graphene, Tool wear, Turning, Surface roughness, MINIMUM QUANTITY LUBRICATION, TOOL WEAR, SURFACE-ROUGHNESS, PERFORMANCE, MQL, SPEED, MACHINABILITY, OPTIMIZATION, PARAMETERS, ENERGY
  • Erciyes Üniversitesi Adresli: Evet

Özet

Cutting fluids significantly improve machining efficiency due to their high contribution to cooling and lubrication. However, the negative effects of traditional cutting fluids and worker health are valid reasons to seek alternatives. One of the alternatives is the minimum quantity lubrication (MQL) system. The MQL system, in which a small quantity of cutting oil is sent to the cutting point with pressure, gives very good results in light and medium cutting conditions. However, the MQL system may be insufficient for turning materials that are difficult to machine, such as M42 steel, which has excellent hardness under high temperature. In such cases, nano additives are added to the coolant to enhance the thermal conductivity and tribological properties of the base coolant. In this study, the effectiveness of graphene nanofluids was investigated comparing with dry and pure MQL to determine the efficiency of graphene nanofluid. In addition, cutting speeds and feed rates of three different levels were used in the experimental design to examine the behavior of the cooling methods under different cutting speeds. After experiments, a hybrid multicriteria decision making approach known as MultiObjective Optimization by Ratio Analysis (MOORA) integrated with Analytic Hierarchy Process (AHP) was used to identify suitable turning parameters. Surface roughness, cutting temperature and tool wear were the output characteristics examined. Further, the morphology of chips was investigated by scanning electron microscope. The experimental results and proposed hybrid AHP-MOORA indicates that graphene nanofluids considerably decrease the surface roughness by 91%, tool wear by 95% and cutting temperature by 82% with dry environment and surface roughness by 66%, tool wear by 86% and cutting temperature by 57% with oil environment in turning M42 steel.