Six sigma optimization of multiple machining characteristics in hard turning under dry, flood, MQL and solid lubrication

Authors

  • Mozammel Mia Mechanical Engineering, Imperial College London, Exhibition Rd., SW7 2AZ London, UK
  • Munish Kumar Gupta Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, PR China
  • Catalin Iulian Pruncu Mechanical Engineering, Imperial College London, Exhibition Rd., SW7 2AZ London, UK
  • Binayak Sen Production Engineering, National Institute of Technology, Agartala 799046, India
  • Aqib Mashood Khan College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Muhammad Jamil College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Shafiul Faraz Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
  • Fahim Asef Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
  • G.M. Shah Imran Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
  • M. Azizur Rahman Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh

Keywords:

Six sigma method, Optimization, Machining, Surface roughness, Cutting temperature, MQL, Solid lubrication

Abstract

The manufacturing industry, especially of automotive sector requires a robust strategy to become cost effective. As a tool for process improvement and reduction of defects while obtaining a major increase in quality, a statistical method can be successfully implemented. It permits removing variability from a process by a field strategy of definemeasureanalyzeimprovemonitor. This study was devoted to originally develop a robust manufacturing process using a statistical optimization i.e. Six sigma to obtain machining with accurate machined surface roughness while keeping the cutting temperature in control to reduce waste of energy. As subject operation, the turning of hardened steel under dry, flood cooling, minimum quality lubrication, and solid lubrication with compressed air condition is considered. The experiments were performed according to Taguchi L8 orthogonal array. The olive oil and graphite were used as liquid and solid lubricant, respectively. It was found that the dry condition can generate good quality surface, but the temperature is dispersive. On the other hand, the MQL showed reduction of temperature gradient. Continuous process control is required to check and balance the depth of cut and cutting speed as they are the two most dominant factors.

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Published

2020-06-30 — Updated on 2020-09-07

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How to Cite

Mia, M. ., Gupta, M. K. ., Pruncu, C. I. ., Sen, B. ., Khan, A. M. ., Jamil, M. ., Faraz, S. ., Asef, F. ., Imran, G. S. ., & Rahman, M. A. . (2020). Six sigma optimization of multiple machining characteristics in hard turning under dry, flood, MQL and solid lubrication. Journal of Production Systems and Manufacturing Science, 1(1), 57-68. Retrieved from http://www.imperialopen.com/index.php/JPSMS/article/view/1 (Original work published June 30, 2020)

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Original Research Articles