3D Research

, 9:46 | Cite as

Modeling and Optimization of Tool Wear and Surface Roughness in Turning of Al/SiCp Using Response Surface Methodology

  • Rashid Ali LaghariEmail author
  • Jianguang Li
  • Zhengyou Xie
  • Shu-qi Wang
3DR Express
Part of the following topical collections:
  1. Modeling


Nowadays metal matrix composites are widely utilized in major industries such as aerospace and automotive because of their excellent properties in association with non-reinforced. This research work is attempted to analyze the consequence of cutting parameters on tool life and surface quality. The experimental work is consist of turning Al/SiCp (45%SiCp) weight with uncoated Carbide tools and the effect of three machining parameters including depth of cut, feed, and speed. Tool life and surface roughness have considered as process response for investigation. The predictive model has been developing to optimize the machining parameters in accordance to Box–Behnken design in Minitab 17, the contour plots the surface plot and response optimizer have made to study the influence of machining parameters and their interactions. ANOVA was carried out to identify the key factor affecting the tool life and surface roughness. The maximum tool life is 10.511 (min) and least surface roughness was observed 0.044 μm. The abrasion and adhesive have the principle wear mechanism observed in machining process. Response surface methodology (RSM) approach have used to optimize the machining parameters, and the RSM model found more than 95% confidence level.


Al/SiCp metal matrix composite Tool life Surface roughness ANOVA Response surface methodology Modeling and optimization 

List of symbols


Metal matrix composite


Response surface methodology


Analysis Of Variance


Depth of cut (mm)


Feed rate (mm/r)


Cutting speed, m/min


Tool life (min)




Silicon carbide particles


  1. 1.
    El-Gallab, M. S., & Sklad, M. P. (2004). Machining of aluminum/silicon carbide particulate metal matrix composites: Part IV. Residual stresses in the machined workpiece. Journal of Materials Processing Technology, 152, 23–34.CrossRefGoogle Scholar
  2. 2.
    Ding, X., Liew, W. Y. H., & Liu, X. D. (2005). Evaluation of machining performance of MMC with PCBN and PCD tools. Wear, 259(7–12), 1225–1234.CrossRefGoogle Scholar
  3. 3.
    Zhu, Y., & Kishawy, H. A. (2005). Influence of alumina particles on the mechanics of machining metal matrix composites. International Journal of Machine Tools and Manufacture, 45, 389–398.CrossRefGoogle Scholar
  4. 4.
    Markopoulos, A. P., Pressas, I. S., Papantoniou, I. G., Karkalos, N. E., & Davim, J. P. (2015). Machining and machining modeling of metal matrix composites—A review. In Modern manufacturing engineering (pp. 99–141). Springer, Cham.Google Scholar
  5. 5.
    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. The International Journal of Advanced Manufacturing Technology, 48(5), 613–624.CrossRefGoogle Scholar
  6. 6.
    Manna, A., & Bhattacharayya, B. (2003). A study on machinability of Al/SiC-MMC. Journal of Materials Processing Technology, 140(1), 711–716.CrossRefGoogle Scholar
  7. 7.
    Davim, J. Paulo, & MonteiroBaptista, A. (2000). Relationship between cutting force and PCD cutting tool wear in machining silicon carbide reinforced aluminium. Journal of Materials Processing Technology, 103(3), 417–423.CrossRefGoogle Scholar
  8. 8.
    Wang, Y., Zhou, X., Sun, M., Zhang, L., & Xiaofei, W. (2017). A new QoE-driven video cache management scheme with wireless cloud computing in cellular networks. Mobile Networks and Applications, 22(1), 72–82.CrossRefGoogle Scholar
  9. 9.
    Srinivasan, A., Arunachalam, R. M., Ramesh, S., & Senthilkumaar, J. S. (2012). Machining performance study on metal matrix composites—A response surface methodology approach. American Journal of Applied Sciences, 9(4), 478–483.CrossRefGoogle Scholar
  10. 10.
    Palaniradja, K., & Alagumoorthi, N. (2012). Study on tool wear and surface roughness in end milling of particulate aluminum metal matrix composite: Application of response surface methodology. Journal of Computational & Applied Research, Mechanical Engineering (JCARME), 2(1), 1–13.Google Scholar
  11. 11.
    Premnath, A. A., Alwarsamy, T., & Sugapriya, K. (2014). A comparative analysis of tool wear prediction using response surface methodology and artificial neural networks. Australian Journal of Mechanical Engineering, 12(1), 38–48.CrossRefGoogle Scholar
  12. 12.
    Suryatheja, P., Srinath, A., & Karthikeyan, S. (2015). Analysis of tool wear while milling hybrid metal matrix composites. Applied Mechanics and Materials, 813–814, 279–284.Google Scholar
  13. 13.
    Antić, A., Šimunović, G., Šarić, T., Milošević, M., & Ficko, M. (2013). A model of tool wear monitoring system for turning. Tehnickivjesnik/Technical Gazette, 20(2), 247–254.Google Scholar
  14. 14.
    Kumar, H., Manna, A., & Kumar, R. (2018). Modeling of process parameters for surface roughness and analysis of machined surface in WEDM of Al/SiC-MMC. Transactions of the Indian Institute of Metals, 71(1), 231–244.CrossRefGoogle Scholar
  15. 15.
    Subramanian, A. V. M., Nachimuthu, M. D. G., & Cinnasamy, V. (2017). Assessment of cutting force and surface roughness in LM6/SiCp using response surface methodology. Journal of Applied Research and Technology, 15(3), 283–296.CrossRefGoogle Scholar
  16. 16.
    Sun, Q., Zhu, H., Li, H., Zhu, H., & Gao, M. (2018). Application of response surface methodology in the optimization of fly ash geopolymer concrete. Revista Română de Materiale/Romanian Journal of Materials, 48(1), 45–52.Google Scholar
  17. 17.
    Rajmohan, T., Palanikumar, K., & Prakash, S. (2013). Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites. Composites Part B Engineering, 50, 297–308.CrossRefGoogle Scholar
  18. 18.
    Stojanovic, B., Blagojevic, J., Babic, M., Velickovic, S., & Miladinovic, S. (2017). Optimization of hybrid aluminum composites wear using Taguchi method and artificial neural network. Industrial Lubrication and Tribology, 69(6), 1005–1015.CrossRefGoogle Scholar
  19. 19.
    Mia, M., Khan, M. A., & Dhar, N. R. (2017). Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti-6Al-4V under cryogenic jets applied at flank and rake faces of coated WC tool. The International Journal of Advanced Manufacturing Technology, 93(1–4), 975–991.CrossRefGoogle Scholar
  20. 20.
    Mia, M., Razi, M. H., Ahmad, I., Mostafa, R., Rahman, S. M., Ahmed, D. H., et al. (2017). Effect of time-controlled MQL pulsing on surface roughness in hard turning by statistical analysis and artificial neural network. The International Journal of Advanced Manufacturing Technology, 91(9–12), 3211–3223.CrossRefGoogle Scholar
  21. 21.
    Mia, M., & Dhar, N. R. (2018). Modeling of surface roughness using RSM, FL and SA in dry hard turning. Arabian Journal for Science and Engineering, 43(3), 1125–1136.CrossRefGoogle Scholar
  22. 22.
    Mia, M. (2018). Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement, 121, 249–260.CrossRefGoogle Scholar
  23. 23.
    Mia, M. (2017). Multi-response optimization of end milling parameters under through-tool cryogenic cooling condition. Measurement, 111, 134–145.CrossRefGoogle Scholar
  24. 24.
    Hung, N. P., Boey, F. Y. C., Khor, K. A., Phua, Y. S., & Lee, H. F. (1996). Machinability of aluminum alloys reinforced with silicon carbide particulates. Journal of Materials Processing Technology, 56(1–4), 966–977.CrossRefGoogle Scholar
  25. 25.
    Dabade, U. A., Joshi, S. S., Balasubramaniam, R., & Bhanuprasad, V. V. (2007). Surface finish and integrity of machined surfaces on Al/SiCp composites. Journal of Materials Processing Technology, 192, 166–174.CrossRefGoogle Scholar
  26. 26.
    Reddy, N. S. K., Kwang-Sup, S., & Yang, M. (2008). Experimental study of surface integrity during end milling of Al/SiC particulate metal–matrix composites. Journal of Materials Processing Technology, 201(1–3), 574–579.CrossRefGoogle Scholar
  27. 27.
    Muthukrishnan, N., Murugan, M., & Rao, K. P. (2008). An investigation on the machinability of Al-SiC metal matrix composites using pcd inserts. The International Journal of Advanced Manufacturing Technology, 38(5–6), 447–454.CrossRefGoogle Scholar
  28. 28.
    Basheer, A. C., Dabade, U. A., Joshi, S. S., Bhanuprasad, V. V., & Gadre, V. M. (2008). Modeling of surface roughness in precision machining of metal matrix composites using ANN. Journal of Materials Processing Technology, 197(1–3), 439–444.CrossRefGoogle Scholar
  29. 29.
    Jeyakumar, S., Marimuthu, K., & Ramachandran, T. (2013). Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM. Journal of Mechanical Science and Technology, 27(9), 2813–2822.CrossRefGoogle Scholar
  30. 30.
    Astakhov, V. P. (2004). The assessment of cutting tool wear. International Journal of Machine Tools and Manufacture, 44(6), 637–647.CrossRefGoogle Scholar
  31. 31.
    Sekulic, M. (2018). Prediction of surface roughness in the ball-end milling process using response surface methodology, genetic algorithms, and grey wolf optimizer algorithm. Advances in Production Engineering & Management, 13(1), 18–30.CrossRefGoogle Scholar
  32. 32.
    Mohan, N. S., & Kulkarni, S. M. (2018). Influence of drilling parameters on torque during drilling of GFRP composites using response surface methodology. Journal of Physics: Conference Series, 953, 012031.Google Scholar
  33. 33.
    Tebassi, H., Yallese, M. A., Meddour, I., Girardin, F., & Mabrouki, T. (2017). On the modeling of surface roughness and cutting force when turning of Inconel 718 using artificial neural network and response surface methodology: Accuracy and benefit. Periodica Polytechnica Mechanical Engineering, 61(1), 1–11.CrossRefGoogle Scholar
  34. 34.
    Tien, C.-L., & Lin, S.-W. (2006). Optimization of process parameters of titanium dioxide films by response surfaces methodology. Optics Communications, 266(2), 574–581.CrossRefGoogle Scholar
  35. 35.
    Hung, N. P., & Zhong, C. H. (1996). Cumulative tool wear in machining metal matrix composites Part I: Modelling. Journal of Materials Processing Technology, 58(1), 109–113.CrossRefGoogle Scholar

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Rashid Ali Laghari
    • 1
    Email author
  • Jianguang Li
    • 1
  • Zhengyou Xie
    • 1
  • Shu-qi Wang
    • 1
  1. 1.School of Mechatronics EngineeringHarbin Institute of TechnologyHarbinChina

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