Skip to main content
SpringerLink
Log in

Statistical analysis and optimization of process parameters in development of metal matrix composite using industrial waste

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Bauxite residue (BR) is the hazardous waste produced during extraction of alumina by processing of bauxite ore. BR has an adverse effect on humans and the environment due to its disposable problem. To minimize the environmental impact, BR must be effectively utilized. One such way is to use BR as a reinforcing for metal matrix composite. In this study, Taguchi’s mixed fractional factorial experimentation (L18) approach is employed in the development of Al6063/BR composite through advanced stir casting process. The process parameters considered are stirring speed (rpm), reinforcement particle size (µm), reinforcement weight fraction (wt%) by weight of the matrix phase, pouring temperature (°C), and preheat temperature (°C). Later, ANOVA results indicate that particle percentage (wt%) is the major contributor in the development of porosity content. Moreover, the interaction of process parameters was also found to have an impact on porosity content. The outcome of the study reveals that the stirring intensity at 350 rpm, particle size at 80 µm, particle percentage at 2 wt%, pouring temperature at 730 °C, and preheat reinforcement temperature at 450 °C are the optimal conditions for fabricating defect free Al6063/BR composite.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

Abbreviations

BR:

Bauxite residue

°C:

Temperature in Celsius

USA:

United state of America

PMMC:

Particulate metal Matrix Composite

MMCs :

Metal Matrix Composite

Wm :

Weight fraction of base matrix

ρm :

Density of base matrix

Wr :

Weight fraction of reinforcement

ρr :

Density of the reinforcement

ρc :

Density of the developed composite

rpm:

Revolution per minute

ν:

Total degree of freedom

νavl :

Available total degree of freedom

S/N:

Signal to noise ratio

S/NLB :

Signal to noise ratio lower is the better

db:

Decibels

SST :

Total Sum of Square

SSA :

Sum of square of parameter A

SSe :

Sum of square of error

Ve:

Total degree of freedom due to error

VT :

Total degree of freedom

VA :

Total degree of freedom due to parameter A

µ:

Mean

p:

Fraction percentage

CICE :

Confidence of interval due to confirmation of experiment

CIPOP :

Confidence of interval due to population

References

  1. Brunori C, Cremisini C, Massanisso P, Pinto V and Torricelli L 2005 Reuse of a treated red mud bauxite waste: studies on environmental compatibility. Journal of Hazardous Materials 117(1): 55–63

    Article  Google Scholar 

  2. Khalil N M and Algamal Y 2019 Recycling of Ceramic Wastes for the Production of High Performance Mullite Refractories. Silicon 1–9

  3. Genç-Fuhrman H, Tjell J C and McConchie D 2004 Adsorption of arsenic from water using activated neutralized red mud. Environmental Science and Technology 38(8): 2428–2434

    Article  Google Scholar 

  4. Verma A S, Suri N M and Kant S 2017 Applications of bauxite residue: a mini-review. Waste Management and Research 35(10): 999–1012

    Article  Google Scholar 

  5. Verma A S, Cheema M S, Kant S and Suri N M 2019 Porosity Study of Developed Al–Mg–Si/Bauxite Residue Metal Matrix Composite Using Advanced Stir Casting Process. Arabian Journal for Science and Engineering 44(2): 1543–1552

    Article  Google Scholar 

  6. Inegbenebor A O, Bolu C A, Babalola P O, Inegbenebor A I and Fayomi O S I 2018 Aluminum silicon carbide particulate metal matrix composite development via stir casting processing. Silicon 10(2): 343–347

    Article  Google Scholar 

  7. Verma A S, Kant S and Suri N M 2015 Corrosion behavior of aluminum base particulate metal matrix composites: a review. Materials Today: Proceedings 2(4–5): 2840–2851

    Google Scholar 

  8. Patel S K, Singh V P, Kumar N, Kuriachen B and Nateriya R 2020 Wear Behaviour of Al-Silicon (LM13) Alloy Composite Reinforcement with TiC and ZrSiO4 Particles. Silicon 12(1): 211–221

    Article  Google Scholar 

  9. Ahmad S N, Hashim J and Ghazali M I 2005 The effects of porosity on mechanical properties of cast discontinuous reinforced metal–matrix composite. Journal of Composite Materials 39(5): 451–466

    Article  Google Scholar 

  10. Ravikumar M, Reddappa H N and Suresh R 2018 Study on Mechanical and Tribological Characterization of Al2O3/SiCp Reinforced Aluminum Metal Matrix Composite. Silicon 10(6): 2535–2545

    Article  Google Scholar 

  11. Hanizam H, Salleh M S, Omar M Z and Sulong A B 2019 Optimization of mechanical stir casting parameters for fabrication of carbon nanotubes–aluminium alloy composite through Taguchi method. Journal of Materials Research and Technology 8(2): 2223–2231

    Article  Google Scholar 

  12. Verma A S, Kant S and Suri N M 2013 Modelling of Process variables for fly ash based Al-6063 composites using Artificial Neural Network. International Journal of Scientific and Research Publications 3(5): 1–5

    Google Scholar 

  13. Ross P J and Ross P J 1988 Taguchi techniques for quality engineering: loss function, orthogonal experiments, parameter and tolerance design (No. TS156 R12). New York: McGraw-Hill

    Google Scholar 

  14. Ramanathan A, Krishnan P K and Muraliraja R 2019 A review on the production of metal matrix composites through stir casting–Furnace design, properties, challenges, and research opportunities. Journal of Manufacturing Processes 42: 213–245

    Article  Google Scholar 

  15. Bandyopadhyay N R, Ghosh S, and Basumallick A 2007 New generation metal matrix composites. Materials and Manufacturing Processes 22(6): 679–682

    Article  Google Scholar 

  16. Guharaja S, Haq A N and Karuppannan K M 2006 Optimization of green sand casting process parameters by using Taguchi’s method. The International Journal of Advanced Manufacturing Technology 30(11–12): 1040–1048

    Article  Google Scholar 

  17. Rekab K and Shaikh M 2005 Statistical design of experiments with engineering applications (pp. 99–104). New York, CRC Press

    Book  Google Scholar 

  18. Adalarasan R and Santhanakumar M 2015 Application of Taguchi based response surface method (TRSM) for optimization of multi responses in drilling Al/SiC/Al2O3 hybrid composite. Journal of the Institution of Engineers (India): Series C. 96(1): 65–71

    Article  Google Scholar 

  19. Kumar S, Satsangi P S and Prajapati D R 2011 Optimization of green sand casting process parameters of a foundry by using Taguchi’s method. The International Journal of Advanced Manufacturing Technology 55(1–4): 23–34

    Article  Google Scholar 

  20. Haq A N, Marimuthu P and Jeyapaul R 2008 Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method. The International Journal of Advanced Manufacturing Technology 37(3–4): 250–255

    Article  Google Scholar 

  21. Enright T P and Prince B 1983 Offline quality control parameter estimation and experimental design with the Taguchi method. AFS Transactions 91: 393–400

    Google Scholar 

  22. Freddi A and Salmon M 2019 Introduction to the Taguchi Method. In: Design, in Design Principles and Methodologies (pp. 159–180). Springer Tracts in Mechanical Engineering, ed. by Choi S B, Duan H, Fu Y, Guardiola C and Sun J Q. Springer, Cham

  23. Krishnaiah K and Shahabudeen P 2012 Applied design of experiments and Taguchi methods. PHI Learning Pvt. Ltd.

    Google Scholar 

  24. Frayce D, Hétu J F and Loong C 1993 Numerical modeling of filling and solidification in die casting. In: 17th International Die Casting Congress and Exposition (pp. 13–17)

Download references

Author information

Authors and Affiliations

  1. Department of Production and Industrial Engineering, Punjab Engineering College, Chandigarh, 160012, India

    AJAY SINGH VERMA, SUMAN KANT & NARENDRA MOHAN SURI

  2. Merck Pharmaceutical, Aubonne, Switzerland

    MANJOT SINGH CHEEMA

Authors
  1. AJAY SINGH VERMA
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. MANJOT SINGH CHEEMA
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. SUMAN KANT
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. NARENDRA MOHAN SURI
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to AJAY SINGH VERMA.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

VERMA, A.S., CHEEMA, M.S., KANT, S. et al. Statistical analysis and optimization of process parameters in development of metal matrix composite using industrial waste. Sādhanā 45, 200 (2020). https://doi.org/10.1007/s12046-020-01439-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-020-01439-6

Keywords

Search

Navigation