Tribological analysis of laser deposited SS316L/Co27Cr6Mo functionally graded materials using adaptive neuro-fuzzy inference system

Pratapa Reddy Yakkaluri; Lakshmi Narayana Kavuluru; Kedar Mallik Mantrala

doi:10.31893/multiscience.2023026

Keywords:

Abstract

Image processing, power engineering, robotics, industrial automation etc., have all found successful uses for artificial intelligence (AI) techniques such as artificial neural networks (ANN) and neuro-fuzzy logic (FL). In this study, an adaptive neuro-fuzzy inference system (ANFIS) modelling of machine learning (ML) has been implemented to estimate the tribological properties of functionally graded materials (FGM). These FGMs were developed using a direct energy deposition (DED) technique of additive manufacturing (AM) from SS316L and Co27Cr6Mo alloys. The input data for this ANFIS modelling is acquired from the experiments done on FGM samples using the Pin on Disc (PoD) apparatus. The main objective of this work is to predict the tribological parameters of FGM samples by creating a data-driven predictive model called ANFIS. From the findings, the ANFIS was found to be the efficient method to estimate the wear rate of FGM samples.
References
1. Alambeigi F, Khadem SM, Khorsand H, Hasan EMS (2016) A comparison of performance of artificial intelligence methods in prediction of dry sliding wear behavior. International Journal of Advanced Manufacturing Technology 84:1981-1994.
2. Aldas K, Ozkul I, Akkurt A (2014) Modelling surface roughness in WEDM process using ANFIS method. Journal of Balkan Tribology Association 20:548-558.
3. Anwar S, Nasr MM, Alkahtani M, Altamimi A (2017) Predicting surface roughness and exit chipping size in BK7 glass during rotary ultrasonic machining by adaptive neuro-fuzzy inference system (ANFIS). Proceedings of the International Conference on Industrial Engineering and Operations Management, Rabat, Morocco, 11-13/4/2017:5773-5785.
4. Dastorani MT, Moghadamnia A, Piri J, Rico-Ramirez M (2010) Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment 166:421-434.
5. Deepak C, Sandeep D, Ashish K, Ramesh KG, Andras K, Rohit K, Tej S (2023) Analysis of fused filament fabrication parameters for sliding wear performance of carbon reinforced polyamide composite material fabricated parts using a hybrid heuristic tool. Polymer Testing, 118:15 January 2023, 107910.
6. Der Jean M, Liu CW, Yang PH, Ho WH (2019) Optimisation of wear behavior of DLC coatings through optimisation of deposition conditions. Materials Science, Medziagotyra 26:269-280.
7. Lei Y, He Z, Zi Y, Hu Q (2007) Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing 21:2280-2294.
8. Masooth P, Jayakumar V, Bharathiraja G, Kumaran P (2022) Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems. Material Research Express, IOP Publishing 9:115002.
9. Nasr MM, Anwar S, Al-Samhan AM, Ghaleb M, Dabwan A (2020) Milling of graphene reinforced ti6al4v nanocomposites:An artificial intelligence-based industry 4.0 approach. Materials (Basel, Switzerland) 13:1-22.
10. Nwobi Okoye CC, Ochieze BQ, Okiy S (2019) Multi-objective optimisation and modelling of age hardening process using ANN, ANFIS and genetic algorithm:results from aluminium alloy A356/cow horn particulate composite. Journal of Materials Research and Technology 8:3054-3075
11. Ragupathy K, Velmurugan C, Ebenezer Jacob Dhas DS, Senthil Kumar N, Dev Wins KL (2021) Prediction of dry sliding wear response of AlMg1SiCu/Silicon Carbide/Molybdenum Disulphide hybrid composites using Adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM). Arabian Journal of Science and Engineering 46:12045-12063.
12. Raju RSS, Rao GS (2017) Assessment of tribological performance of coconut shell ash particle reinforced Al-Si-Fe composites using grey-fuzzy approach. Tribology in Industry 39:364-377.
13. Ramesh CS, Jain VKS, Keshavamurthy R, Khan ZA, Hadfield M (2013) Prediction of slurry erosive wear behaviour of Al6061 alloy using a fuzzy logic approach. WIT Transactions of Engineering Sciences 78:109-119.
14. Ramesh CS, Mohan N, Jain VKS, Gudi HR (2014) Adaptive neural network for estimation of sliding wear behaviour of Al6061-carbon fibre composites. Applied Mechanics and Materials 592:1267-1271.
15. Sada SO, Ikpeseni SC (2021) Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance. Heliyon 7:e06136.
16. Seputra YEA, Soegijono B (2019) Engineering of aluminium matrix composite (AMC) reinforcement organoclay based on hotpress method using adaptive neuro-fuzzy inference system (ANFIS). IOP Conference Series:Materials Science and Engineering, Volume 509, 13th Joint Conference on Chemistry, 7-8/9/2018, Semarang, Indonesia:509/1/ID:012156.
17. Sosimi AA, Gbenebor OP, Oyerinde O, Bakare OO, Adeosun SO, Olaleye SA (2020) Analysing wear behaviour of Al-CaCO3 composites using ANN and Sugeno-type fuzzy inference systems. Neural Computing and Applications 32:13453-13464.
18. Velmurugan N, Muniappan A, Harikrishna KL, Sakthivel TG (2021) Surface roughness modelling in wire EDM machining aluminium of Al6061 composite by ANFIS. Conference: International conference “2022 IEEE International Conference on Nanoelectronics, Nano-photonics, Nanomaterials, Nano-bioscience & Nanotechnology” at IEEE Photonics Society Student Chapter, Mangalam College of Engineering on 28-29/4/2022. (Article in Press).
19. Vijaya Kumar S, Karunamoorthy L (2012) Modelling wear behaviour of Al-SiC metal matrix composites: Soft computing technique. Tribolology-Materials, Surfaces and Interfaces 6:25-30.
20. Zhuang X, Yu T, Sun Z, Song K, (2021) Wear prediction of a mechanism with multiple joints based on ANFIS. Engineering Failure Analysis 119:104958.
21. Zid K, Ben Ahmed M, Turki M (2018) Modeling of flank wear using ANFIS. ACM International Conference Proceeding Series.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Yakkaluri, P. R., Kavuluru, L. N., & Mantrala, K. M. (2023). Tribological analysis of laser deposited SS316L/Co27Cr6Mo functionally graded materials using adaptive neuro-fuzzy inference system. Multidisciplinary Science Journal, 5(3), 2023026. https://doi.org/10.31893/multiscience.2023026

[1] Alambeigi F, Khadem SM, Khorsand H, Hasan EMS (2016) A comparison of performance of artificial intelligence methods in prediction of dry sliding wear behavior. International Journal of Advanced Manufacturing Technology 84:1981-1994.

[2] Aldas K, Ozkul I, Akkurt A (2014) Modelling surface roughness in WEDM process using ANFIS method. Journal of Balkan Tribology Association 20:548-558.

[3] Anwar S, Nasr MM, Alkahtani M, Altamimi A (2017) Predicting surface roughness and exit chipping size in BK7 glass during rotary ultrasonic machining by adaptive neuro-fuzzy inference system (ANFIS). Proceedings of the International Conference on Industrial Engineering and Operations Management, Rabat, Morocco, 11-13/4/2017:5773-5785.

[4] Dastorani MT, Moghadamnia A, Piri J, Rico-Ramirez M (2010) Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment 166:421-434.

[5] Deepak C, Sandeep D, Ashish K, Ramesh KG, Andras K, Rohit K, Tej S (2023) Analysis of fused filament fabrication parameters for sliding wear performance of carbon reinforced polyamide composite material fabricated parts using a hybrid heuristic tool. Polymer Testing, 118:15 January 2023, 107910.

[6] Der Jean M, Liu CW, Yang PH, Ho WH (2019) Optimisation of wear behavior of DLC coatings through optimisation of deposition conditions. Materials Science, Medziagotyra 26:269-280.

[7] Lei Y, He Z, Zi Y, Hu Q (2007) Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing 21:2280-2294.

[8] Masooth P, Jayakumar V, Bharathiraja G, Kumaran P (2022) Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems. Material Research Express, IOP Publishing 9:115002.

[9] Nasr MM, Anwar S, Al-Samhan AM, Ghaleb M, Dabwan A (2020) Milling of graphene reinforced ti6al4v nanocomposites:An artificial intelligence-based industry 4.0 approach. Materials (Basel, Switzerland) 13:1-22.

[10] Nwobi Okoye CC, Ochieze BQ, Okiy S (2019) Multi-objective optimisation and modelling of age hardening process using ANN, ANFIS and genetic algorithm:results from aluminium alloy A356/cow horn particulate composite. Journal of Materials Research and Technology 8:3054-3075

[11] Ragupathy K, Velmurugan C, Ebenezer Jacob Dhas DS, Senthil Kumar N, Dev Wins KL (2021) Prediction of dry sliding wear response of AlMg1SiCu/Silicon Carbide/Molybdenum Disulphide hybrid composites using Adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM). Arabian Journal of Science and Engineering 46:12045-12063.

[12] Raju RSS, Rao GS (2017) Assessment of tribological performance of coconut shell ash particle reinforced Al-Si-Fe composites using grey-fuzzy approach. Tribology in Industry 39:364-377.

[13] Ramesh CS, Jain VKS, Keshavamurthy R, Khan ZA, Hadfield M (2013) Prediction of slurry erosive wear behaviour of Al6061 alloy using a fuzzy logic approach. WIT Transactions of Engineering Sciences 78:109-119.

[14] Ramesh CS, Mohan N, Jain VKS, Gudi HR (2014) Adaptive neural network for estimation of sliding wear behaviour of Al6061-carbon fibre composites. Applied Mechanics and Materials 592:1267-1271.

[15] Sada SO, Ikpeseni SC (2021) Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance. Heliyon 7:e06136.

[16] Seputra YEA, Soegijono B (2019) Engineering of aluminium matrix composite (AMC) reinforcement organoclay based on hotpress method using adaptive neuro-fuzzy inference system (ANFIS). IOP Conference Series:Materials Science and Engineering, Volume 509, 13th Joint Conference on Chemistry, 7-8/9/2018, Semarang, Indonesia:509/1/ID:012156.

[17] Sosimi AA, Gbenebor OP, Oyerinde O, Bakare OO, Adeosun SO, Olaleye SA (2020) Analysing wear behaviour of Al-CaCO3 composites using ANN and Sugeno-type fuzzy inference systems. Neural Computing and Applications 32:13453-13464.

[18] Velmurugan N, Muniappan A, Harikrishna KL, Sakthivel TG (2021) Surface roughness modelling in wire EDM machining aluminium of Al6061 composite by ANFIS. Conference: International conference “2022 IEEE International Conference on Nanoelectronics, Nano-photonics, Nanomaterials, Nano-bioscience & Nanotechnology” at IEEE Photonics Society Student Chapter, Mangalam College of Engineering on 28-29/4/2022. (Article in Press).

[19] Vijaya Kumar S, Karunamoorthy L (2012) Modelling wear behaviour of Al-SiC metal matrix composites: Soft computing technique. Tribolology-Materials, Surfaces and Interfaces 6:25-30.

[20] Zhuang X, Yu T, Sun Z, Song K, (2021) Wear prediction of a mechanism with multiple joints based on ANFIS. Engineering Failure Analysis 119:104958.

[21] Zid K, Ben Ahmed M, Turki M (2018) Modeling of flank wear using ANFIS. ACM International Conference Proceeding Series.

Abstract

References

How to cite