Skip to main content
SpringerLink
Log in

Energy consumption model for cutting operations in a stochastic environment

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

Abstract

Nowadays, everyone agrees that it is urgent to reduce the consumption of energy and raw materials when manufacturing industries are concerned. Among all the transformation technologies, those related to chip removal are particularly interesting because of the high volume of processed material and because the final quality of the products largely depends on the fact that these processes correspond to the final stages of the production chain. Compromising the quality of the piece at this stage means not only discarding the piece but also losing the energy used to prepare the raw piece and to carry out the previous processes. Since unsuitable use of productive resources leads to a waste of time and money, in the past, many researchers have been developing models to optimize production processes by maximizing productivity and/or minimizing costs. Today, however, it is necessary to optimize the same processes from the total energy consumption point of view. Many authors already addressed this problem using a deterministic approach, when trying to identify the optimal cutting conditions. This means that tools are considered to be completely reliable elements in the production processes. The present work proposes an alternative methodology based on a stochastic approach to describe the tool resource; this approach is able to take into consideration the actual resources reliability and the consequent penalties deriving from their unpredicted failure, occurring before the expected replacement time.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Abbreviations

a p :

Depth of cut

C 60 :

Cutting speed for a unitary tool life

C tool :

Cost of the tool in terms of insert and tool stem

C work :

Total machining cost per unit of time

F(α):

Cumulative function distribution of tool life

E(N):

Average number of parts that can be cut by one tool in the stochastic environment

E :

Total energy consumption to complete the production

E aux :

Energy for auxiliary operations at the end of the production

E c :

Cutting energy

E fluid :

Energy for lubricant flush

Epen :

Energy penalty due to the premature tool breakage (its life is less than hα)

E rough1 :

Energy required for producing one rough part

E s :

Set-up energy

E tc :

Tool change energy

E tool1 :

Energy required for producing one tool

f :

Feed rate

f(h):

Probability density function

h :

Generic tool life in the stochastic environment

h α :

Expected tool life corresponding to R(α) reliability

h α, j :

Tool substitution interval

h i :

Generic tool life generated by Monte Carlo simulation

h m :

Average value of tool life

h m, k :

Average value of tool life for a specific simulation

h p :

Average productive life of a tool in the stochastic environment

h R :

Average tool life of those tools that break before hα

i :

Tool counter

j :

General number of parts performed by a single tool

k :

Simulation counter

MRR:

Material removal rate

n p :

Number of parts to be produced

n t :

Number of tools needed to carry out the entire production

N(hm, k, σk):

Normal distribution of tool life

P idle :

Absorbed power when the machine is idle

P work :

Absorbed power when the machine is working

\( \dot{q_f} \) :

Dielectric fluid flow rate

R(α):

Tool reliability

r :

Exponent related to tool and workpiece materials

s :

Chip section

T :

Time needed to develop a certain tool flank wear

t :

Total production time

t aux :

Time for auxiliary operations (e.g., cleaning the machine at the end of the work shift)

t c1 :

Time needed to machine a single part

t c :

Total cutting time

t s :

Machine setup time

t tc :

Time for tool change

V :

Material volume to be cut

V 1 :

Material volume to be cut for one part

v c :

Cutting speed

v c, k :

Specific cutting speed applied to a specific tool i

\( {v}_{c_{C\min }} \) :

Optimal cutting speed able to assure the cost minimization

\( {v}_{c_{P\max }} \) :

Optimal cutting speed able to assure the productivity maximization

w :

Experimental exponent

σ :

Standard deviation for the tool life distribution

σ k :

Standard deviation of a specific simulation

References

  1. Gutowski TG, Sahni S, Allwood JM, Ashby MF, Worrell E (2013) The energy required to produce materials: constraints on energy-intensity improvements, parameters of demand. In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, p 371(1986). https://doi.org/10.1098/rsta.2012.0003

    Chapter  Google Scholar 

  2. Kellens K, Dewulf W, Overcash M, Hauschild MZ, Duflou JR (2012) Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory (UPLCI) CO2PE! initiative (cooperative effort on process emissions in manufacturing). Part 2: Case studies. Int J Life Cycle Assess 17(2):242–251. https://doi.org/10.1007/s11367-011-0352-0

    Article  Google Scholar 

  3. Balogun VA, Mativenga PT (2013) Modelling of direct energy requirements in mechanical machining processes. J Clean Prod 41:179–186. https://doi.org/10.1016/j.jclepro.2012.10.015

    Article  Google Scholar 

  4. Lawal SA, Choudhury IA, Nukman Y (2013) A critical assessment of lubrication techniques in machining processes: a case for minimum quantity lubrication using vegetable oil-based lubricant. J Clean Prod 41:210–221. https://doi.org/10.1016/j.jclepro.2012.10.016

    Article  Google Scholar 

  5. Lawal SA, Choudhury IA, Nukman Y (2014) Evaluation of vegetable and mineral oil-in-water emulsion cutting fluids in turning AISI 4340 steel with coated carbide tools. J Clean Prod 66:610–618. https://doi.org/10.1016/j.jclepro.2013.11.066

    Article  Google Scholar 

  6. Davoodi B, Tazehkandi AH (2014) Experimental investigation and optimization of cutting parameters in dry and wet machining of aluminum alloy 5083 in order to remove cutting fluid. J Clean Prod 68:234–242. https://doi.org/10.1016/j.jclepro.2013.12.056

    Article  Google Scholar 

  7. Kuram E, Ozcelik B, Bayramoglu M, Demirbas E, Simsek BT (2013) Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments. J Clean Prod 42:159–166. https://doi.org/10.1016/j.jclepro.2012.11.003

    Article  Google Scholar 

  8. Jeswiet J, Kara S (2008) Carbon emissions and CESTM in manufacturing. In: CIRP Annals - Manufacturing Technology, vol 57, pp 17–20. https://doi.org/10.1016/j.cirp.2008.03.117

    Chapter  Google Scholar 

  9. Dahmus JB, Gutowski TG (2004) An environmental analysis of machining. In: American Society of Mechanical Engineers, Manufacturing Engineering Division, MED, vol 15. Anaheim, California, pp 643–652. https://doi.org/10.1115/IMECE2004-62600

    Chapter  Google Scholar 

  10. Hu S, Liu F, He Y, Hu T (2012) An on-line approach for energy efficiency monitoring of machine tools. J Clean Prod 27:133–140. https://doi.org/10.1016/j.jclepro.2012.01.013

    Article  Google Scholar 

  11. Li L, Yan J, Xing Z (2013) Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. J Clean Prod 52:113–121. https://doi.org/10.1016/j.jclepro.2013.02.039

    Article  Google Scholar 

  12. Winter M, Li W, Kara S, Herrmann C (2014) Determining optimal process parameters to increase the eco-efficiency of grinding processes. J Clean Prod 66:644–654. https://doi.org/10.1016/j.jclepro.2013.10.031

    Article  Google Scholar 

  13. Velchev S, Kolev I, Ivanov K, Gechevski S (2014) Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning. J Clean Prod 80:139–149. https://doi.org/10.1016/j.jclepro.2014.05.099

    Article  Google Scholar 

  14. Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. Energy 2 Retrieved from http://web.mit.edu/ebm/www/Publications/CIRP_2006.pdf

  15. Li C, Chen X, Tang Y, Li L (2017) Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost. J Clean Prod 140:1805–1818. https://doi.org/10.1016/j.jclepro.2016.07.086

    Article  Google Scholar 

  16. 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. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 268–273. https://doi.org/10.1007/978-3-642-19692-8-47

    Chapter  Google Scholar 

  17. Liu F, Liu S (2012) Multi-period energy model of electro-mechanical main driving system during the service process of machine tools. Jixie Gongcheng Xuebao J Mech Eng 48(21):132–140. https://doi.org/10.3901/JME.2012.21.132

    Article  Google Scholar 

  18. Rajemi MF, Mativenga PT, Aramcharoen A (2010) Sustainable machining: selection of optimum turning conditions based on minimum energy considerations. J Clean Prod 18(10–11):1059–1065. https://doi.org/10.1016/j.jclepro.2010.01.025

    Article  Google Scholar 

  19. Schmid, S. R., & Kalpakjian, S. (2015). Manufacturing manufacturing engineering and technology SI by Serope Kalpakjian 7th Pearson 2013.

    Google Scholar 

  20. Hinduja S, Sandiford D (2004) An optimum two-tool solution for milling 2 1/2D features from technological and geometric viewpoints. CIRP Ann Manuf Technol 53(1):77–80. https://doi.org/10.1016/S0007-8506(07)60649-0

    Article  Google Scholar 

  21. Mativenga PT, Rajemi MF (2011) Calculation of optimum cutting parameters based on minimum energy footprint. In: CIRP Annals - Manufacturing Technology, vol 60, pp 149–152. https://doi.org/10.1016/j.cirp.2011.03.088

    Chapter  Google Scholar 

  22. Priarone PC, Robiglio M, Settineri L (2017) Re-thinking the maximum efficiency in machining when environmental stress is accounted for. In: XIII Convegno dell’Associazione Italiana di Tecnologia Meccanica AITEM 2017. Pisa, Italy, pp 11–13

    Google Scholar 

  23. Priarone PC, Robiglio M, Settineri L, Tebaldo V (2016) Modelling of specific energy requirements in machining as a function of tool and lubricoolant usage. In: CIRP Annals - Manufacturing Technology, vol 65, pp 25–28. https://doi.org/10.1016/j.cirp.2016.04.108

    Chapter  Google Scholar 

  24. Kalpakjian, S., & Schmid, S. (2014). Manufacturing Engineering & Technology (7th Edition).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio 7/b, 24044, Dalmine, BG, Italy

    Mariangela Quarto, Gianluca D’Urso & Claudio Giardini

Authors
  1. Mariangela Quarto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gianluca D’Urso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudio Giardini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gianluca D’Urso.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Quarto, M., D’Urso, G. & Giardini, C. Energy consumption model for cutting operations in a stochastic environment. Int J Adv Manuf Technol 110, 2743–2752 (2020). https://doi.org/10.1007/s00170-020-06075-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-06075-2

Keywords

Search

Navigation