Optimization of Minimum Quantity Lubricant Parameters in Turning for the Minimization of Cutting Zone Temperature

  • سال انتشار: 1391
  • محل انتشار: ماهنامه بین المللی مهندسی، دوره: 25، شماره: 4
  • کد COI اختصاصی: JR_IJE-25-4_030
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 781
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نویسندگان

t Tamizharasan

Principal, TRP Engineering College, Irungalur, Tiruchirappall - ۶۲۱ ۱۰۵

j Kingston Barnabas

Department of Mechanical Engineering, Anjalai Ammal Mahalingam Engineering College, Kovilvenni, Tiruvarur District – ۶۱۴ ۴۰۳

چکیده

The use of cutting fluid in manufacturing industries has now become more problematic due to environmental pollution and health related problems of employees. The minimization of cutting fluidalso leads to the saving of lubricant cost and cleaning time of machine, tool and work-piece. Theconcept of Minimum Quantity Lubricant (MQL) has come in to practice since a decade ago in order toovercome the disadvantages of flood cooling. This experimental investigation deals with the effects ofMQL parameters during turning for the minimization of cutting zone temperature by consideringsurface roughness as constraint. The selected MQL parameters are varied through four levels. The maximum temperature values during machining in all the test conditions as per L16 orthogonal array are recorded. The best levels of selected MQL parameters for the minimization of cutting zonetemperature were identified using Taguchi’s Design of Experiments. A validation experiment is conducted with the identified best levels of parameters and the corresponding cutting zone temperature is recorded. This analysis further inter-relates the performances of Particle Swarm Optimization (PSO),Simulated Annealing Algorithm (SAA) and Differential Evolution (DE) for the minimization of cutting zone temperature. The results obtained from DE are comparatively better than that of the results obtained from other techniques

کلیدواژه ها

Cutting Zone Temperature,Surface Roughness,Taguchi’s Design Of Experiments,Particle Swarm Optimization,Simulated Annealing Algorithm,Differential Evolution

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