Skip to main content
SpringerLink
Log in

Exergy analysis and multi-objective optimization of air cooling system for dry machining

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

For an air cooling system that is applied in dry machining, the thermodynamic properties of cold compressed air have an important influence on the cooling of the cutting region. In this study, the air cooling system of the dry cutting machine tool is mathematically modeled and analyzed in terms of the exergy and economic aspects of the system design and component selection. With the exergy analysis of system components, the relations between system components parameters and thermodynamic properties of compressed air are obtained. The exergy functions are verified to be acceptable for industrial application by comparing measured values with calculated values of the thermodynamic properties of cold compressed air. A multi-objective optimization process is carried out using GA (genetic algorithm) combined with the Euclidean technique and the TOPSIS (technique for order preference by similarity to ideal solution) decision-making method. Exergetic efficiency and total cost rate of the air cooling system are the objectives, while the thermodynamic properties of cold compressed air supplied to the cutting region are the constraints. The results show that an optimum solution with an exergetic efficiency of 55.1% and total cost rate of 9.37 × 10−4 US$/s is achieved. Furthermore, air compressor, aftercooler, and air refrigerator are the components with the highest exergy destruction rate and capital cost, and have great potential for further improvement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Jegatheesan V, Liow JL, Shu L, Kim SH, Visvanathan C (2009) The need for global coordination in sustainable development. J Clean Prod 17(7):637–643. doi:10.1016/j.jclepro.2008.11.016

    Article  Google Scholar 

  2. Jozić S, Bajić D, Celent L (2015) Application of compressed cold air cooling: achieving multiple performance characteristics in end milling process. J Clean Prod 100:325–332. doi:10.1016/j.jclepro.2015.03.095

    Article  Google Scholar 

  3. Klocke F, Eisenblätter G (1997) Dry cutting. CIRP Ann Manuf Technol 46(2):519–526. doi:10.1016/S0007-8506(07)60877-4

    Article  Google Scholar 

  4. Thamke D, Schirsch R, Zielasko W (1998) Wirtschaftlichkeit der trockenbearbeitung. VDI Ber 1375:371–397

    Google Scholar 

  5. Weinert K, Adams FJ, Thamke D (1995) Was kostet die Kühlschmierung? Tech 44(7):19–23

    Google Scholar 

  6. Klocke F, Lung D, Puls H (2013) FEM-modelling of the thermal workpiece deformation in dry turning. Procedia CIRP 8:240–245. doi:10.1016/j.procir.2013.06.096

    Article  Google Scholar 

  7. Sreejith PS, Ngoi BKA (2000) Dry machining: machining of the future. J Mater Process Technol 101(1):287–291. doi:10.1016/S0924-0136(00)00445-3

    Article  Google Scholar 

  8. Weinert K, Inasaki I, Sutherland JW, Wakabayashi T (2004) Dry machining and minimum quantity lubrication. CIRP Ann Manuf Technol 53(2):511–537. doi:10.1016/S0007-8506(07)60027-4

    Article  Google Scholar 

  9. Liew WYH, Hutchings IM, Williams JA (1998) Friction and lubrication effects in the machining of aluminium alloys. Tribol Lett 5(1):117–122. doi:10.1023/A:1019164918708

    Article  Google Scholar 

  10. Cao H, Zhu L, Li X, Chen P, Chen Y (2016) Thermal error compensation of dry hobbing machine tool considering workpiece thermal deformation. Int J Adv Manuf Technol 86(5):1739–1751. doi:10.1007/s00170-015-8314-5

    Article  Google Scholar 

  11. Sukaylo V, Kaldos A, Pieper HJ, Bana V, Sobczyk M (2005) Numerical simulation of thermally induced workpiece deformation in turning when using various cutting fluid applications. J Mater Process Technol 167(2):408–414. doi:10.1016/j.jmatprotec.2005.05.042

    Article  Google Scholar 

  12. Zhu L, Cao H, Zeng D et al (2017) Multi-variable driving thermal energy control model of dry hobbing machine tool. Int J Adv Manuf Technol. doi:10.1007/s00170-017-0086-7

  13. Sun S, Brandt M, Dargusch MS (2010) Machining Ti–6Al–4V alloy with cryogenic compressed air cooling. Int J Mach Tools Manuf 50(11):933–942. doi:10.1016/j.ijmachtools.2010.08.003

    Article  Google Scholar 

  14. Liu NM, Chiang KT, Hung CM (2013) Modeling and analyzing the effects of air-cooled turning on the machinability of Ti–6Al–4V titanium alloy using the cold air gun coolant system. Int J Adv Manuf Technol 67(5):1053–1066. doi:10.1007/s00170-012-4547-8

    Article  Google Scholar 

  15. Rubio EM, Agustina B, Marín M, Bericua A (2015) Cooling systems based on cold compressed air: a review of the applications in machining processes. Procedia Eng 132:413–418. doi:10.1016/j.proeng.2015.12.513

    Article  Google Scholar 

  16. Yalçın B, Özgür AE, Koru M (2009) The effects of various cooling strategies on surface roughness and tool wear during soft materials milling. Mater Des 30(3):896–899. doi:10.1016/j.matdes.2008.05.037

    Article  Google Scholar 

  17. Vittorini D, Cipollone R (2016) Energy saving potential in existing industrial compressors. Energy 102:502–515. doi:10.1016/j.energy.2016.02.115

    Article  Google Scholar 

  18. Azizifar S, Banooni S (2016) Modeling and optimization of industrial multistage compressed air system using actual variable effectiveness in hot regions. Adv Eng Mech Eng 8(5). doi:10.1177/1687814016647231

  19. Zahlan J, Asfour S (2015) A multi-objective approach for determining optimal air compressor location in a manufacturing facility. J Manuf Syst 35:176–190. doi:10.1016/j.jmsy.2015.01.003

    Article  Google Scholar 

  20. Han J, Wang L, Wang H, Cheng N (2012) A new thermal error modeling method for CNC machine tools. Int J Adv Manuf Technol 62(1):205–212. doi:10.1007/s00170-011-3796-2

    Article  Google Scholar 

  21. Ruijun L, Wenhua Y, Zhang HH, Qifan Y (2012) The thermal error optimization models for CNC machine tools. Int J Adv Manuf Technol 63(9):1167–1176. doi:10.1007/s00170-012-3978-6

    Article  Google Scholar 

  22. Wu CW, Tang CH, Chang CF, Shiao YS (2012) Thermal error compensation method for machine center. Int J Adv Manuf Technol 59(5):681–689. doi:10.1007/s00170-011-3533-x

    Article  Google Scholar 

  23. Pusavec F, Krajnik P, Kopac J (2010) Transitioning to sustainable production—part I: application on machining technologies. J Clean Prod 18(2):174–184. doi:10.1016/j.jclepro.2009.08.010

    Article  Google Scholar 

  24. Kotas TJ (1985) The exergy method of thermal plant analysis. Butterworth Publishers, Stoneham, pp 288–292

    Google Scholar 

  25. Keenan JH, Chao, Kaye (1983) Gas tables: international version: thermodynamic properties of air products of combustion and component gases compressible flow functions. Wiley, New York.

  26. Engineers RA (2012) ASHRAE handbook: HVAC systems and equipment. ASHRAE, Atlanta

    Google Scholar 

  27. Min S, Qin H, Wang G, Hu S (2005) Analysis of energy conservation technology of desiccant dryers for compressed air systems. Shanghai Energy Conserv 4:106–110

    Google Scholar 

  28. Engineers RA (2009) ASHRAE handbook: fundamentals. ASHRAE, Atlanta

    Google Scholar 

  29. Shirazi A, Najafi B, Aminyavari M, Rinaldi F, Taylor R (2014) Thermal–economic–environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling. Energy 69(51):212–226. doi:10.1016/j.energy.2014.02.071

    Article  Google Scholar 

  30. Incropera FP, DeWitt DP, Bergman TL, Lavine AS (2007) Fundamentals of heat and mass transfer, 6th edn. J. Wiley, New York

    Google Scholar 

  31. Colburn AP, De EIDP (1933) Mean temperature difference and heat transfer coefficient in liquid heat exchangers. Indonesia 25:873–877. doi:10.1021/ie50284a010

    Google Scholar 

  32. Winterton RHS (1998) Where did the Dittus and Boelter equation come from? Int J Heat Mass Transf 41(4–5):809–810. doi:10.1016/S0017-9310(97)00177-4

    Article  Google Scholar 

  33. Moran MJ, Shapiro HN (1992) Fundamentals of engineering thermodynamics, 2nd ed. Wiley, New York

  34. Martin H (1977) Heat and mass transfer between impinging gas jets and solid surfaces. Adv Heat Trans 13:1–60. doi:10.1016/S0065-2717(08)70221-1

    Article  Google Scholar 

  35. Roosen P, Uhlenbruck S, Lucas K (2003) Pareto optimization of a combined cycle power system as a decision support tool for trading off investment vs. operating costs. Int J Therm Sci 42(6):553–560. doi:10.1016/S1290-0729(03)00021-8

    Article  Google Scholar 

  36. Wall G (1991) Optimization of refrigeration machinery. Int J Refrig 14(6):336–340. doi:10.1016/0140-7007(91)90029-G

    Article  Google Scholar 

  37. Selbaş R, Kızılkan Ö, Şencan A (2006) Thermoeconomic optimization of subcooled and superheated vapor compression refrigeration cycle. Energy 31(12):2108–2128. doi:10.1016/j.energy.2005.10.015

    Article  Google Scholar 

  38. Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007. doi:10.1016/j.ress.2005.11.018

    Article  Google Scholar 

  39. Najafi H, Najafi B (2010) Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm. Heat Mass Transf 46(6):639–647. doi:10.1007/s00231-010-0612-8

    Article  Google Scholar 

  40. Selleri T, Najafi B, Rinaldi F, Colombo G (2013) Mathematical modeling and multi-objective optimization of a mini-channel heat exchanger via genetic algorithm. J Therm Sci Eng Appl 5(3):1–10. doi:10.1115/1.4023893

    Article  Google Scholar 

  41. Najafi H, Najafi B, Hoseinpoori P (2011) Energy and cost optimization of a plate and fin heat exchanger using genetic algorithm. Appl Therm Eng 31(10):1839–1847. doi:10.1016/j.applthermaleng.2011.02.031

    Article  Google Scholar 

  42. Sayyaadi H, Mehrabipour R (2012) Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger. Energy 38(1):362–375. doi:10.1016/j.energy.2011.11.048

    Article  Google Scholar 

  43. Yue Z (2011) A method for group decision-making based on determining weights of decision makers using topsis. Appl Therm Eng 35(4):1926–1936. doi:10.1016/j.apm.2010.11.001

    MathSciNet  MATH  Google Scholar 

  44. Navidbakhsh M, Shirazi A, Sanaye S (2013) Four E analysis and multi-objective optimization of an ice storage system incorporating PCM as the partial cold storage for air-conditioning applications. Appl Therm Eng 58(1–2):30–41. doi:10.1016/j.applthermaleng.2013.04.002

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Libin Zhu, Huajun Cao & Xiao Yang

  2. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, China

    Haihong Huang

Authors
  1. Libin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huajun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haihong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huajun Cao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, L., Cao, H., Huang, H. et al. Exergy analysis and multi-objective optimization of air cooling system for dry machining. Int J Adv Manuf Technol 93, 3175–3188 (2017). https://doi.org/10.1007/s00170-017-0731-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-017-0731-1

Keywords

Search

Navigation