Indian Journal of Engineering & Materials Sciences

Open Access Open Access  Restricted Access Subscription Access

Surface Roughness Prediction in Grinding Ti Using ANFIS Hybrid Algorithm


Affiliations
1 Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai 603 203, India
 

Intelligent manufacturing is needed, and many techniques and tools have been developed with this in mind. Over time, many of these techniques have been combined, and hybrid approaches have provided better results in shorter times, leading to a more precise prediction of outcomes when compared to the use of individual tools. This research focused on grinding Ti-6Al-4V workpiece material with a Carbon nanotube (CNT) incorporated grinding wheel. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to predict surface roughness which was taken as the output of choice for this study. A new hybrid of ANFIS with Genetic Algorithm (ANFIS-GA) was then proposed to see if this prediction method could obtain greater precision. The regression analysis predicted the experimental model’s linear relationship to surface roughness, and the effect of grinding process parameters on surface roughness was analysed using the sensitivity analysis method.

Keywords

ANFIS, CNT Grinding Wheel, Fuzzy Logic, Regression Analysis, Sensitivity Analysis, Taguchi Analysis.
User
Notifications
Font Size

  • Jesuthanam C P, Kumanan S & Asokan P, Mach Sci Technol, 11(2) (2007) 271.

  • Hashmi K,Graham I D, Mills B & Hashmi M S J, J Mater Process Technol,142 (1)(2003) 152.

  • Prabhu S, Uma M & Vinayagam B K, Neural Comput Appl, 26 (1) (2014) 41.

  • Dao T P, Math Probl Eng, (2016).

  • Li Y, Shieh M D, Yang C C & Zhu L, Math Probl Eng, (2018).

  • Chau N Le, Dao T P & Nguyen V T T, Math Probl Eng, (2018).

  • Pandiyan V, Caesarendra W, Glowacz A. & Tjahjowidodo T, Symmetry (Basel),12(1)(2020).

  • Baraheni M, Azarhoushang B, Daneshi A, Kadivar M & Amini S, Int J Adv Manuf Technol, (2021) 2823.

  • Baseri H & Alinejad G, World Acad Sci Eng Technol, 73(1) (2011) 499.

  • Baseri H, Int J Abras Technol, 3(4) (2010) 316.

  • Dambatta Y S, Sayuti M, Sarhan A A D, Ab Shukor H Bin, Derahman N A binti & Manladan S M, Ind Lubr Tribo,71(2) (2019) 309.

  • Mamalis A G, Vogtländer L O G & Markopoulos A, Precis Eng, 28(1) (2004) 16.

  • You Jiali, Carbon Nanotube Grinding Wheel for Nano Machining of Engineering and Biomedical Materials, PhD thesis, Hong Kong University of Science and Technology, 2009.

  • Bakshi S R & Agarwal A, Carbon, 49(2) (2011) 533.

  • Bansal S, Kumar N & Jindal P, Mater Today Proc, 4(2) (2017), 3867.

  • https://www.predev.com/pdffiles/surface_roughness_terminology_and_parameters.pdf (18 May 2020).


Abstract Views: 149

PDF Views: 96




  • Surface Roughness Prediction in Grinding Ti Using ANFIS Hybrid Algorithm

Abstract Views: 149  |  PDF Views: 96

Authors

Deborah Serenade Stephen
Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai 603 203, India
Sethuramalingam Prabhu
Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai 603 203, India

Abstract


Intelligent manufacturing is needed, and many techniques and tools have been developed with this in mind. Over time, many of these techniques have been combined, and hybrid approaches have provided better results in shorter times, leading to a more precise prediction of outcomes when compared to the use of individual tools. This research focused on grinding Ti-6Al-4V workpiece material with a Carbon nanotube (CNT) incorporated grinding wheel. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to predict surface roughness which was taken as the output of choice for this study. A new hybrid of ANFIS with Genetic Algorithm (ANFIS-GA) was then proposed to see if this prediction method could obtain greater precision. The regression analysis predicted the experimental model’s linear relationship to surface roughness, and the effect of grinding process parameters on surface roughness was analysed using the sensitivity analysis method.

Keywords


ANFIS, CNT Grinding Wheel, Fuzzy Logic, Regression Analysis, Sensitivity Analysis, Taguchi Analysis.

References