Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems
This research work investigates the mechanical and wear behaviour of graphene (C) and zirconium di-oxide (ZrO _2 ) reinforced Aluminium alloy 6061 hybrid nano composites (AMMHNCs) fabricated by ultrasonic-assisted stir casting method. Graphene and ZrO _2 are selected as reinforcements for increasing...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022-01-01
|
| Series: | Materials Research Express |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2053-1591/ac9c86 |
| _version_ | 1797746219195301888 |
|---|---|
| author | Haja Syeddu Masooth P Jayakumar V G Bharathiraja Kumaran Palani |
| author_facet | Haja Syeddu Masooth P Jayakumar V G Bharathiraja Kumaran Palani |
| author_sort | Haja Syeddu Masooth P |
| collection | DOAJ |
| description | This research work investigates the mechanical and wear behaviour of graphene (C) and zirconium di-oxide (ZrO _2 ) reinforced Aluminium alloy 6061 hybrid nano composites (AMMHNCs) fabricated by ultrasonic-assisted stir casting method. Graphene and ZrO _2 are selected as reinforcements for increasing the wear resistance and hardness of the base alloy AA6061. The mixing proportions of graphene and ZrO _2 reinforced with AA6061 in weight are 100% AA6061/0% Graphene/0% ZrO _2 , 98.5% AA6061/0.5% Graphene/1% ZrO _2 , 97.5% AA6061/0.5% Graphene/2% ZrO _2 , 98% AA6061/1% Graphene/1% ZrO _2 , 97% AA6061/1% Graphene/2% ZrO _2. Microstructural study was carried out using optical and scanning electron microscopic images to analyse the dispersion of reinforcements in the composite. The results shown that, ultrasonic-assisted stir casting method improves the uniformity in dispersion of reinforcements. The hardness, tensile, impact and wear test were carried out based on ASTM standards to analyse the properties in the proposed composite specimens. It was observed that, the hardness, tensile strength and impact strength are increases by 21.88%, 69.42% and 78.57% respectively and percentage elongation is decreased by 63.52% with the increase of reinforcements. Wear resistance increases with the increase of reinforcements. In order to analyse the wear behaviour originality of new composite under wear test parameters, Artificial Neural Network (ANN) and Artificial Neuro Fuzzy Inference Systems (ANFIS) models were used to predict the wear rate for experimented and non-experimented parameters. The prediction analysis was useful in studying the wear behaviour of the composite. Comparative analysis for ANN and ANFIS was performed and the results shown that, ANFIS model predicted with accuracy of R ^2 with 99.9%. |
| first_indexed | 2024-03-12T15:34:48Z |
| format | Article |
| id | doaj.art-d611aa46d3c54d6ba2e4f7527848c38d |
| institution | Directory Open Access Journal |
| issn | 2053-1591 |
| language | English |
| last_indexed | 2024-03-12T15:34:48Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Materials Research Express |
| spelling | doaj.art-d611aa46d3c54d6ba2e4f7527848c38d2023-08-09T16:18:59ZengIOP PublishingMaterials Research Express2053-15912022-01-0191111500210.1088/2053-1591/ac9c86Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systemsHaja Syeddu Masooth P0Jayakumar V1G Bharathiraja2Kumaran Palani3https://orcid.org/0000-0002-7978-1617Institute of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India; Department of Mechanical Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, No. 1 Jawaharlal Nehru Road, Vadapalani, TN, IndiaDepartment of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham , Chennai, IndiaInstitute of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, IndiaDepartment of Mechanical Engineering, College of Engineering, Wolaita Sodo University , Wolaita Sodo, PO Box:138, EthiopiaThis research work investigates the mechanical and wear behaviour of graphene (C) and zirconium di-oxide (ZrO _2 ) reinforced Aluminium alloy 6061 hybrid nano composites (AMMHNCs) fabricated by ultrasonic-assisted stir casting method. Graphene and ZrO _2 are selected as reinforcements for increasing the wear resistance and hardness of the base alloy AA6061. The mixing proportions of graphene and ZrO _2 reinforced with AA6061 in weight are 100% AA6061/0% Graphene/0% ZrO _2 , 98.5% AA6061/0.5% Graphene/1% ZrO _2 , 97.5% AA6061/0.5% Graphene/2% ZrO _2 , 98% AA6061/1% Graphene/1% ZrO _2 , 97% AA6061/1% Graphene/2% ZrO _2. Microstructural study was carried out using optical and scanning electron microscopic images to analyse the dispersion of reinforcements in the composite. The results shown that, ultrasonic-assisted stir casting method improves the uniformity in dispersion of reinforcements. The hardness, tensile, impact and wear test were carried out based on ASTM standards to analyse the properties in the proposed composite specimens. It was observed that, the hardness, tensile strength and impact strength are increases by 21.88%, 69.42% and 78.57% respectively and percentage elongation is decreased by 63.52% with the increase of reinforcements. Wear resistance increases with the increase of reinforcements. In order to analyse the wear behaviour originality of new composite under wear test parameters, Artificial Neural Network (ANN) and Artificial Neuro Fuzzy Inference Systems (ANFIS) models were used to predict the wear rate for experimented and non-experimented parameters. The prediction analysis was useful in studying the wear behaviour of the composite. Comparative analysis for ANN and ANFIS was performed and the results shown that, ANFIS model predicted with accuracy of R ^2 with 99.9%.https://doi.org/10.1088/2053-1591/ac9c86AA6061/graphene/ZrO2ultrasonic stir castingmechanical propertieswear behaviorANNANFIS |
| spellingShingle | Haja Syeddu Masooth P Jayakumar V G Bharathiraja Kumaran Palani Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems Materials Research Express AA6061/graphene/ZrO2 ultrasonic stir casting mechanical properties wear behavior ANN ANFIS |
| title | Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems |
| title_full | Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems |
| title_fullStr | Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems |
| title_full_unstemmed | Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems |
| title_short | Investigations on mechanical and wear behaviour of graphene and zirconia reinforced AA6061 hybrid nanocomposites using ANN and Sugeno-type fuzzy inference systems |
| title_sort | investigations on mechanical and wear behaviour of graphene and zirconia reinforced aa6061 hybrid nanocomposites using ann and sugeno type fuzzy inference systems |
| topic | AA6061/graphene/ZrO2 ultrasonic stir casting mechanical properties wear behavior ANN ANFIS |
| url | https://doi.org/10.1088/2053-1591/ac9c86 |
| work_keys_str_mv | AT hajasyeddumasoothp investigationsonmechanicalandwearbehaviourofgrapheneandzirconiareinforcedaa6061hybridnanocompositesusingannandsugenotypefuzzyinferencesystems AT jayakumarv investigationsonmechanicalandwearbehaviourofgrapheneandzirconiareinforcedaa6061hybridnanocompositesusingannandsugenotypefuzzyinferencesystems AT gbharathiraja investigationsonmechanicalandwearbehaviourofgrapheneandzirconiareinforcedaa6061hybridnanocompositesusingannandsugenotypefuzzyinferencesystems AT kumaranpalani investigationsonmechanicalandwearbehaviourofgrapheneandzirconiareinforcedaa6061hybridnanocompositesusingannandsugenotypefuzzyinferencesystems |