Investigation on surface roughness, tool wear and cutting power in MQL turning of bio-medical Ti-6Al-4V ELI alloy with sustainability

被引:22
作者
Rajan, K. M. [1 ,2 ]
Sahoo, Ashok Kumar [1 ]
Routara, Bharat Chandra [1 ]
Kumar, Ramanuj [1 ]
机构
[1] Kalinga Inst Ind Technol KIIT Deemed Univ, Sch Mech Engn, Bhubaneswar 751024, Odisha, India
[2] Cent Tool Room & Training Ctr CTTC, Bhubaneswar, Odisha, India
关键词
MQL; Ti-6Al-4V ELI; machinability; grey relational analysis; quadratic regression; sustainability; MINIMUM QUANTITY LUBRICATION; CHIP FORMATION; PARAMETERS; MACHINABILITY; OPTIMIZATION; PERFORMANCE; NANOFLUIDS; STRATEGIES;
D O I
10.1177/09544089211063712
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Ti-6Al-4V ELI (Grade 23) is highly recommended for bio-materials and due to its low thermal conductivity and chemically reactive properties, machinability is poor. Thus the current work emphasized on the selection of appropriate cooling technique and optimal cutting parameters for machining of Ti-6Al-4V ELI alloy with sustainability analysis for surface roughness, flank wear and cutting power. Initially, the cutting performances under dry, flood and MQL environments are compared and MQL is observed to better performed. At lower speed (70 m/min), MQL exhibited 26.38% and 19.69% lesser surface roughness relative to dry and flood cooling individually. At the same cutting condition, MQL assisted cutting resulted in lower flank wear relative to dry (157. 33%) and flood cooling (40%). Further, a detailed investigation has been made under MQL through Taguchi L-18 design of experiments. The major mechanisms for flank wear are found to be abrasion, chipping and notch wear. Optimal data set through Grey relational analysis is found to be v(1) (70 m/min), f(1) (0.1 mm/rev) and d(1) (0.1 mm) and improved. Quadratic regression model is found to be significant for prediction of responses. Sustainability Pugh matrix assessment revealed that MQL environment enhanced the economical, technological as well as environmental and operator health aspects. Reduction of energy consumption by 53.96% and savings of carbon footprints by 68.46 kg of CO2 observed under MQL at optimal conditions and thus saves manufacturing cost.
引用
收藏
页码:1452 / 1466
页数:15
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