Skip to main content
SpringerLink
Log in

Neuro-fuzzy predictive model for surface roughness and cutting force of machined Al–20 Mg2Si–2Cu metal matrix composite using additives

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Today’s metal matrix composites are widely used due to their excellent properties, which are useful for high-performance applications in the automotive and aerospace industries. Furthermore, improving the machinability of these composites is vital for improving product quality in the manufacturing process. In this research, various adaptive network-based fuzzy inference systems (ANFISs) were introduced to evaluate the effect of the feed rate, the cutting speed and the particle size on the machinability of Al–20 Mg2Si metal matrix composite. Two ANFIS models were selected as the most precise models for predicting surface roughness and cutting force. Results show that the proposed ANFIS models have an adequate accuracy in predicting the machinability of metal matrix composites.

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
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Hwang YK, Lee CM, Park SH (2009) Evaluation of machinability according to the changes in machine tools and cooling lubrication environments and optimization of cutting conditions using Taguchi method. Int J Precis Eng Manuf 10(3):65

    Article  Google Scholar 

  2. Gupta A, Singh H, Aggarwal A (2011) Taguchi-fuzzy multi output optimization (MOO) in high speed CNC turning of AISI P-20 tool steel. Expert Syst Appl 38(6):6822–6828

    Article  Google Scholar 

  3. Palanikumar K, Karthikeyan R (2007) Assessment of factors influencing surface roughness on the machining of Al/SiC particulate composites. Mater Des 28(5):1584–1591

    Article  Google Scholar 

  4. Nur AN, Saeed F, Tuty A, Esah H (2014) Effect of strontium in refining of Mg2Si particulate reinforced Al–20% Mg2Si in situ composite. Am Eurasian J Sustain Agric 8(11):1–5

    Google Scholar 

  5. Farahany S, Ghandvar H, Nordin NA, Ourdjini A, Idris MH (2016) Effect of primary and eutectic Mg2Si crystal modifications on the mechanical properties and sliding wear behaviour of an Al–20Mg2Si–2Cu–xBi composite. J Mater Sci Technol 32(11):1083–1097

    Article  Google Scholar 

  6. Seeman M, Ganesan G, Karthikeyan R, Velayudham A (2010) Study on tool wear and surface roughness in machining of particulate aluminum metal matrix composite-response surface methodology approach. Int J Adv Manuf Technol 48(5–8):613–624

    Article  Google Scholar 

  7. Ozben T, Kilickap E, Cakır O (2008) Investigation of mechanical and machinability properties of SiC particle reinforced Al-MMC. J Mater Process Technol 198(1–3):220–225

    Article  Google Scholar 

  8. Barzani MM, Sarhan AA, Farahany S, Ramesh S, Maher I (2015) Investigating the machinability of Al–Si–Cu cast alloy containing bismuth and antimony using coated carbide insert. Measurement 62:170–178

    Article  Google Scholar 

  9. Barzani MM, Farahany S, Yusof NM, Ourdjini A (2013) The influence of bismuth, antimony, and strontium on microstructure, thermal, and machinability of aluminum-silicon alloy. Mater Manuf Process 28(11):1184–1190

    Article  Google Scholar 

  10. Marani M, Songmene V, Kouam J, Zedan Y (2018) Experimental investigation on microstructure, mechanical properties and dust emission when milling Al–20Mg 2 Si–2Cu metal matrix composite with modifier elements. Int J Adv Manuf Technol 99(1–4):789–802

    Article  Google Scholar 

  11. Zeinali M, Mazlan SA, Fatah AYA, Zamzuri H (2013) A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system. Smart Mater Struct 22(12):125013

    Article  Google Scholar 

  12. Khanlou HM, Ang BC, Barzani MM, Silakhori M, Talebian S (2015) Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System. Neural Comput Appl 26(7):1751–1761

    Article  Google Scholar 

  13. Pourtousi M, Zeinali M, Ganesan P, Sahu JN (2015) Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS. Rsc Adv 5(104):85652–85672

    Article  Google Scholar 

  14. Dong M, Wang N (2011) Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Appl Math Model 35(3):1024–1035

    Article  Google Scholar 

  15. Ho WH, Tsai JT, Lin BT, Chou JH (2009) Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Syst Appl 36(2):3216–3222

    Article  Google Scholar 

  16. Çaydaş U, Hasçalık A, Ekici S (2009) An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Syst Appl 36(3):6135–6139

    Article  Google Scholar 

  17. Maher I, Sarhan AA, Barzani MM, Hamdi M (2015) Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production. J Clean Prod 108:247–255

    Article  Google Scholar 

  18. Barzani MM, Zalnezhad E, Sarhan AA, Farahany S, Ramesh S (2015) Fuzzy logic based model for predicting surface roughness of machined Al–Si–Cu–Fe die casting alloy using different additives-turning. Measurement 61:150–161

    Article  Google Scholar 

  19. Salur E, Aslan A, Kuntoglu M, Gunes A, Sahin OS (2019) Experimental study and analysis of machinability characteristics of metal matrix composites during drilling. Compos Part B Eng 166:401–413

    Article  Google Scholar 

  20. Bai W, Roy A, Sun R, Silberschmidt VV (2019) Enhanced machinability of SiC-reinforced metal-matrix composite with hybrid turning. J Mater Process Technol 268:149–161

    Article  Google Scholar 

  21. Azwadi CSN, Zeinali M, Safdari A, Kazemi A (2013) Adaptive-network-based fuzzy inference system analysis to predict the temperature and flow fields in a lid-driven cavity. Numer Heat Transf Part A Appl 63(12):906–920

    Article  Google Scholar 

  22. Nordin NA, Farahany S, Abubakar T, Hamzah E (2015) Alteration by cerium element on primary and eutectic Mg2Si phases in Al–20% Mg2Si in situ composite. Adv Mater Res 1125:23

    Article  Google Scholar 

  23. Rath D, Panda S, Pal K (2018) Prediction of surface quality using chip morphology with nodal temperature signatures in hard turning of AISI D3 steel. Mater Today Proc 5(5):12368–12375

    Article  Google Scholar 

  24. Kannan S, Kishawy HA (2008) Tribological aspects of machining aluminum metal matrix composites. J Mater Process Technol 198(1–3):399–406

    Article  Google Scholar 

  25. Barzani MM, Farahany S, Songmene V (2017) Machinability characteristics, thermal and mechanical properties of Al–Mg2Si in situ composite with bismuth. Measurement 110:263–274

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Street West, Montreal, Canada

    Mohsen Marani, Victor Songmene, Jules Kouam & Yasser Zedan

  2. VSE Research Laboratory, Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia (UTM), 54100, Kuala Lumpur, Malaysia

    Mohammadjavad Zeinali

Authors
  1. Mohsen Marani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victor Songmene
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammadjavad Zeinali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jules Kouam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yasser Zedan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohsen Marani.

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

Marani, M., Songmene, V., Zeinali, M. et al. Neuro-fuzzy predictive model for surface roughness and cutting force of machined Al–20 Mg2Si–2Cu metal matrix composite using additives. Neural Comput & Applic 32, 8115–8126 (2020). https://doi.org/10.1007/s00521-019-04314-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04314-6

Keywords

Search

Navigation