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Sustainable machining as a mean of reducing the environmental impacts related to the energy consumption of the machine tool: a case study of AISI 1045 steel machining

  • Carmita Camposeco-NegreteEmail author
  • Juan de Dios Calderón-Nájera
ORIGINAL ARTICLE
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Abstract

Due to the rising concerns related to the depletion of fossil fuel sources, and the climate change associated to the usage of such sources for electricity generation, there is a high pressure for diminishing the energy consumption of all the industrial sectors in order to mitigate the negative impacts associated to the energy required to manufacture a product. Computer numerical control (CNC) machining accounts for a larger portion of the total energy drawn from the grid by the manufacturing sector. Therefore, the energy efficiency of the manufacturing operations should be enhanced to optimize their energy consumption and to reduce their environmental burden as well. As a mean for achieving sustainable manufacturing operations, the present paper outlines an experimental study to optimize cutting parameters in turning of AISI 1045 steel. One of the main objectives was to minimize the total specific energy consumed by the CNC machine tool during material removal, considering dry cutting to decrease the environmental impacts linked to the coolant usage. As a measure of the surface quality, the second objective was to reduce the average surface roughness of the workpiece. The same material removal volume was considered for all the experimental trials. The response surface method was used to obtain the regression models for all the variables studied, and the desirability method was selected for defining the values of these variables, named the cutting parameters, that minimized the quantity of electrical energy consumed, and surface roughness. The results achieved showed that it is possible to obtain a more sustainable machining process without sacrificing the productivity of the process and the final quality of the product.

Keywords

Energy consumption reduction Turning Sustainable manufacturing Response surface method 

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Notes

Funding

The corresponding author would like to thank the ITESM Campus Toluca, the Consejo Nacional de Ciencia y Tecnologia (CONACyT) and the Consejo Mexiquense de Ciencia y Tecnología (COMECyT) for providing financial resources.

References

  1. 1.
    Balogun VA, Mativenga PT (2013) Modelling of direct energy requirements in mechanical machining processes. J Clean Prod 41:179–186CrossRefGoogle Scholar
  2. 2.
    Anastas P, Zimmerman J (2004) Design through the twelve principles of green engineering. Environ Sci Technol 37:94–101CrossRefGoogle Scholar
  3. 3.
    Li W, Zein A, Kara S, Herrmann C (2011) An investigation into fixed energy consumption of machine tools. In: Glocalized solutions for sustainability in manufacturing: proceedings of the 18th CIRP international conference on life cycle engineering. pp 268–273Google Scholar
  4. 4.
    Liu ZY, Guo YB, Sealy MP, Liu ZQ (2016) Energy consumption and process sustainability of hard milling with tool wear progression. J Mater Process Technol 229:305–312CrossRefGoogle Scholar
  5. 5.
    Zhang Y, Ren S, Liu Y, Sakao T, Huisingh D (2017) A framework for big data driven product lifecycle management. J Clean Prod 159:229–240CrossRefGoogle Scholar
  6. 6.
    Owodunni O (2017) Awareness of energy consumption in manufacturing processes. Procedia Manuf 8:152–159CrossRefGoogle Scholar
  7. 7.
    Sun W, Zhang F (2016) Design and thermodynamic analysis of a flash power system driven by process heat of continuous casting grade steel billet. Energy 116:94–101CrossRefGoogle Scholar
  8. 8.
    Sealy MP, Liu ZY, Zhang D, Guo YB, Liu ZQ (2016) Energy consumption and modeling in precision hard milling. J Clean Prod 135:1591–1601CrossRefGoogle Scholar
  9. 9.
    Duflou JR, Sutherland JW, Dornfeld D, Herrmann C, Jeswiet J, Kara S, Hasuchild M, Kellens K (2012) Towards energy and resource efficient manufacturing: a processes and systems approach. CIRP Ann Manuf Technol 61-2:587–609CrossRefGoogle Scholar
  10. 10.
    Duflou JR, Kellens K, Renaldi GY, Dewulf W (2012) Critical comparison of methods to determine the energy input for discrete manufacturing processes. CIRP Ann Manuf Technol 61:63–66CrossRefGoogle Scholar
  11. 11.
    Apostolos F, Alexios P, Georgios P, Panagiotis S, George C (2013) Energy efficiency of manufacturing processes: a critical review. Procedia CIRP 7:628–633CrossRefGoogle Scholar
  12. 12.
    Li L, Li C, Tang Y, Yi Q (2017) Influence factors and operational strategies for energy efficiency improvement of CNC machining. J Clean Prod 161:220–238CrossRefGoogle Scholar
  13. 13.
    Bhattacharya A, Das S, Majumber P, Batish A (2009) Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA. Prod Eng Res Dev 3:31–40CrossRefGoogle Scholar
  14. 14.
    Fratila D, Caizar C (2011) Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg3. J Clean Prod 19:640–645CrossRefGoogle Scholar
  15. 15.
    Mativenga PT, Rajemi MF (2011) Calculation of optimum cutting parameters based on minimum energy footprint. CIRP Ann Manuf Technol 60:149–152CrossRefGoogle Scholar
  16. 16.
    Asiltürk I, Neseli S (2012) Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis. Measurement 45:785–794CrossRefGoogle Scholar
  17. 17.
    Hanafi I, Khamlichi A, Mata Cabrera F, Almansa E, Jabbouri A (2012) Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools. J Clean Prod 33:1–9CrossRefGoogle Scholar
  18. 18.
    Bhushan RK (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242–254CrossRefGoogle Scholar
  19. 19.
    Yan J, Li L (2013) Multi-objective optimization of milling parameters e the tradeoffs between energy, production rate and cutting quality. J Clean Prod 52:462–471CrossRefGoogle Scholar
  20. 20.
    Campatelli G, Lorenzini L, Scippa A (2014) Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel. J Clean Prod 66:309–316CrossRefGoogle Scholar
  21. 21.
    Priarone PC, Robiglio M, Settineri L, Tebaldo V (2016) Modelling of specific energy requirements in machining as a function of tool and lubricoolant usage. CIRP Ann Manuf Technol 65:25–28CrossRefGoogle Scholar
  22. 22.
    Camposeco-Negrete C, de Dios Calderón Nájera J, Miranda-Valenzuela JC (2016) Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design. Int J Adv Manuf Technol 83:1341–1347CrossRefGoogle Scholar
  23. 23.
    Bilga PS, Singh S, Kumar R (2016) Optimization of energy consumption response parameters for turning operation using Taguchi method. J Clean Prod 137:1406–1417CrossRefGoogle Scholar
  24. 24.
    Wang B, Liu Z, Song Q, Wan Y, Shi Z (2016) Proper selection of cutting parameters and cutting tool angle to lower the specific cutting energy during high speed machining of 7050-T7451 aluminum alloy. J Clean Prod 129:292–304CrossRefGoogle Scholar
  25. 25.
    Jia S, Yuan Q, Lv J, Liu Y, Ren D, Zhang Z (2017) Therblig-embedded value stream mapping method for lean energy machining. Energy 138:1081–1098CrossRefGoogle Scholar
  26. 26.
    Lv J, Tang R, Tang W, Jia S, Liu Y, Cao Y (2018) An investigation into methods for predicting material removal energy consumption in turning. J Clean Prod 193:128–139CrossRefGoogle Scholar
  27. 27.
    Heidenhain (2010) Aspects of energy efficiency in machine tools. Technical information. http://www.heidenhain.us/wp-content/uploads/25-AspectsofEnergyEfficiencyinMT.pdf. Accessed 02.12.18
  28. 28.
    Weinert K, Inasaki I, Sutherland JW, Wakabayashi T (2004) Dry machining and minimum quantity lubrication. CIRP Ann Manuf Technol 53-2:511–537CrossRefGoogle Scholar
  29. 29.
    Diniz AE, Micaroni R (2002) Cutting conditions for finish turning process aiming: the use of dry cutting. Int J Mach Tool Manu 42(8):899–904CrossRefGoogle Scholar
  30. 30.
    Nayak SK, Patro JK, Dewangan S, Gangopadhyay S (2014) Multi-objective optimization of machining parameters during dry turning of AISI 304 austenitic stainless steel using grey relational analysis. Procedia Mater Sci 6:701–708CrossRefGoogle Scholar
  31. 31.
    Carou D, Rubio EM, Lauro CH, Davim JP (2014) Experimental investigation on surface finishing during intermittent turning of UNS M11917 magnesium alloy under dry and near dry machining conditions. Measurement 56:136–154CrossRefGoogle Scholar
  32. 32.
    Sharma J, Sidhu BS (2014) Investigation of effects of dry and near dry machining on AISI D2 steel using vegetable oil. J Clean Prod 66:619–623CrossRefGoogle Scholar
  33. 33.
    Patil NG, Asem A, Pawade RS, Thakur DG, Brahmankar PK (2014) Comparative study of high speed machining of Inconel 718 in dry condition and by using compressed cold carbon dioxide gas as coolant. Procedia CIRP 24:86–91CrossRefGoogle Scholar
  34. 34.
    Qehaja N, Jakupi K, Bunjaku A, Bruci M, Osmani H (2015) Effect of machining parameters and machining time on surface roughness in dry turning process. Procedia Eng 100:135–140CrossRefGoogle Scholar
  35. 35.
    Montgomery DC (2009) Design and analysis of experiments. John Wiley & Sons, Inc., New YorkGoogle Scholar
  36. 36.
    Gaitonde VN, Karnik SR, Figueira L, Davim JP (2009) Machinability investigations in hard turning of AISI D2 cold work tool steel with conventional and wiper ceramic inserts. Int J Refract Met Hard Mater 27:754–763CrossRefGoogle Scholar
  37. 37.
    Sahu NK, Andhare AB (2018) Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms. J Comput Design Eng 6:1–12.  https://doi.org/10.1016/j.jcde.2018.04.004 CrossRefGoogle Scholar
  38. 38.
    Saravanakumar A, Karthikeyan SC, Dhamotharan B, Gokul kumar V (2018) Optimization of CNC turning parameters on aluminum alloy 6063 using Taguchi robust design. Mater Today-Proc 5:8290–8298CrossRefGoogle Scholar
  39. 39.
    Nataraj M, Balasubramanian K, Palanisamy D (2018) Optimization of machining parameters for CNC turning of Al/Al2O3 MMC using RSM approach. Mater Today-Proc 5(14265):14272Google Scholar
  40. 40.
    Box GEP, Hunter JS (1957) Multifactor experimental designs for exploring response surfaces. Ann Math Stat 28:195–242CrossRefGoogle Scholar
  41. 41.
    Correia AE, Davim JP (2011) Surface roughness measurement in turning carbon steel AISI 1045 using wiper inserts. Measurement 44:1000–1005CrossRefGoogle Scholar
  42. 42.
    Sandvik Coromant (2018) DCMT 11 T3 08-PM 4225 CoroTurn ® 107 insert for turning. https://www.sandvik.coromant.com/en-us/products/Pages/productdetails.aspx?c=DCMT%2011%20T3%2008-PM%20%20%20%204225. Accessed 05.12.18
  43. 43.
    National Instruments (2018) USB-6211 multifunction I/O device. http://www.ni.com/en-us/support/model.usb-6211.html. Accessed 05.12.12
  44. 44.
    National Instruments (2018) What is LabVIEW? www.ni.com/en-us/shop/labview.html. Accessed 05.12.18
  45. 45.
    Camposeco-Negrete C (2015) Optimization of cutting parameters using response surface method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. J Clean Prod 91:109–117CrossRefGoogle Scholar
  46. 46.
    Chabbi A, Yallese MA, Meddour I, Nouioua M, Mabrouki T, Girardin F (2017) Predictive modeling and multi-response optimization of technological parameters in turning of polyoxymethylene polymer (POM C) using RSM and desirability function. Measurement 95:99–115CrossRefGoogle Scholar
  47. 47.
    Sengottuvel P, Satishkumar S, Dinakaran D (2013) Optimization of multiple characteristics of edm parameters based on desirability approach and fuzzy modeling. Procedia Eng 64:1069–1078CrossRefGoogle Scholar
  48. 48.
    Elbah M, Yallese MA, Aouici H, Mabrouki T, Rigal JF (2013) Comparative assessment of wiper and conventional ceramic tools on surface roughness in hard turning AISI 4140 steel. Measurement 46:3041–3056CrossRefGoogle Scholar
  49. 49.
    Tank K, Shetty N, Panchal G, Tukrel A (2018) Optimization of turning parameters for the finest surface roughness characteristics using desirability function analysis coupled with fuzzy methodology and ANOVA. Mater Today-Proc 5:13015–13024CrossRefGoogle Scholar
  50. 50.
    Behrendt T, Zein A, Min S (2012) Development of an energy consumption monitoring procedure for machine tools. CIRP Ann Manuf Technol 61:43–46CrossRefGoogle Scholar
  51. 51.
    Sandvik Coromant (2010) Corokey. Sandvik, Fair LawnGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Carmita Camposeco-Negrete
    • 1
  • Juan de Dios Calderón-Nájera
    • 1
  1. 1.Automotive Engineering Research Center (CIMA)Tecnológico de Monterrey Campus TolucaTolucaMexico

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