Comparative Analysis of Response Surface Methodology and Artificial Neural Network on the Wear Properties of Surface Composite Fabricated by Friction Stir Processing

Tyagi, Lakshay; Butola, Ravi; Kem, Luckshaya; Singari, Ranganath M.

doi:10.1007/s40735-020-00469-1

Cited by 24 publications

(13 citation statements)

References 42 publications

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“…Composites are defined as a multiphase system that consists of two different groups of materials, one is the matrix and another is reinforcement, which is chemically and physically distinct and separated by interfaces (Kumar et al , 2018; Butola et al , 2020d). Nowadays, aluminium metal matrix composites (AMMCs) are finding wide application in automobile, marine, aerospace, defense and even in the nuclear industry (Butola et al , 2020a; Tyagi et al , 2021). AMMCs are preferred over aluminium alloys because of their excellent strength-to-weight ratio (Butola et al , 2020b).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of microhardness and wear properties of Al 6063 composite reinforced with yttrium oxide using stir casting process

Butola

Yuvaraj

Singh

et al. 2021

WJE

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Purpose This study aims to analyse the changes in mechanical and wear performance of aluminium alloy when yttrium oxide particles are incorporated. The microstructures are studied to analyse the change in the grain structures. Worn surfaces are observed via scanning electron microscope to study the wear mechanism in detail. Design/methodology/approach Stir casting is used to incorporate varying composition of yttrium particles, having an average particle size of 25 micrometer, in aluminium alloy 6063 matrix. Wear testing is carried out by DUCOM manufactured high temperature rotatory tribometer, and an indentation test is used for analysing the microhardness of the fabricated samples. Findings Microhardness of the material is increased with the increasing content of particulate addition. With the increasing content of reinforcement, more refined grains are produced. The load is transferred from the matrix to more rigid yttrium oxide particles. These factors contributed to escalated microhardness of the reinforced samples. Particulate addition enhanced the wear performance of the material; this might be attributed to increased microhardness and formation of an oxide layer. Originality/value Aluminium composites are finding wide applications in various industries, and there is always a requirement of material with enhanced tribological properties. Yttrium oxide particles exhibit improved mechanical properties, and their interaction with the aluminium matrix has not been studied much in the past. So, in this work, yttrium oxide incorporated aluminium matrix is studied.

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Section: Introductionmentioning

confidence: 99%

Evaluation of microhardness and wear properties of Al 6063 composite reinforced with yttrium oxide using stir casting process

Butola

Yuvaraj

Singh

et al. 2021

WJE

Self Cite

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show abstract

“…Different algorithms could be used for training the ANN along with GBR and SVR, and it can be used to compare the results. [79] in one. However, this model may not perform well for many inputs, i.e., this model fails in a big data paradigm.…”

Section: Future Work Includes the Optimum Coating Properties Dependent On The Aps Process Parameters [78]mentioning

confidence: 99%

“…No data MATLAB [79] Table 7: Advantages and disadvantages of the above-discussed algorithms (Table 6) used to evaluate wear behavior in different metals.…”

Section: Supervised Learning Ree Inputs Ten Hidden Layers and Two Outputsmentioning

confidence: 99%

A Cutting-Edge Survey of Tribological Behavior Evaluation Using Artificial and Computational Intelligence Models

Selvaraj

Raj

Dharnidharka

et al. 2021

Advances in Materials Science and Engineering

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Any metal surface’s usefulness is essential in various applications such as machining and welding and aerospace and aerodynamic applications. There is a great deal of wear in metals, used widely in machines and appliances. The gradual loss of the upper metal layers in all metal parts is inevitable over the machine or component’s lifetime. Artificial intelligence implementations and computational models are being studied to evaluate different metals’ tribological behavior, as technological progress has been made in this field. Different neural networks were used for different metals. They are classified in this paper, together with a description of their benefits and inconveniences and an overview and use of the different types of wear. Artificial intelligence is a relatively new term that uses mechanical engineering. There is still no scientific progress to examine various metal wear cases and compare AI and computational models’ accuracy in wear behavior.

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“…Dinaharan et al [53] applied an ANN model to predict the tribological properties of FSPS made of copper matrix and ceramic additives such as Al 2 O 3 , SiC, WC, TiC, and B 4 C. The input process parameters of the model were ceramic particle, tool rotational speed, traverse speed, and groove width, while the model output was wear rate. ANN was compared with response surface methodology (RSM) to predict the wear behavior of FSPS made of aluminum reinforced by ceramic additives [54]. The inputs of the models were tool rotational speed, sliding speed, and load, while the model outputs were wear rate and coefficient of friction.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Multilayer Perceptrons Model Using Grey Wolf Optimizer to Predict Mechanical and Microstructural Properties of Friction Stir Processed Aluminum Alloy Reinforced by Nanoparticles

et al. 2021

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In the current investigation, AA2024 aluminum alloy is reinforced by alumina nanoparticles using a friction stir process (FSP) with multiple passes. The mechanical properties and microstructure observation are conducted experimentally using tensile, microhardness, and microscopy analysis methods. The impacts of the process parameters on the output responses, such as mechanical properties and microstructure grain refinement, were investigated. The effect of multiple FSP passes on the grain refinement, and various mechanical properties are evaluated, then the results are conducted to train a hybrid artificial intelligence predictive model. The model consists of a multilayer perceptrons optimized by a grey wolf optimizer to predict mechanical and microstructural properties of friction stir processed aluminum alloy reinforced by alumina nanoparticles. The inputs of the model were rotational speed, linear processing speed, and number of passes; while the outputs were grain size, aspect ratio, microhardness, and ultimate tensile strength. The prediction accuracy of the developed hybrid model was compared with that of standalone multilayer perceptrons model using different error measures. The developed hybrid model shows a higher accuracy compared with the standalone model.

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Comparative Analysis of Response Surface Methodology and Artificial Neural Network on the Wear Properties of Surface Composite Fabricated by Friction Stir Processing

Cited by 24 publications

References 42 publications

Evaluation of microhardness and wear properties of Al 6063 composite reinforced with yttrium oxide using stir casting process

Evaluation of microhardness and wear properties of Al 6063 composite reinforced with yttrium oxide using stir casting process

A Cutting-Edge Survey of Tribological Behavior Evaluation Using Artificial and Computational Intelligence Models

An Optimized Multilayer Perceptrons Model Using Grey Wolf Optimizer to Predict Mechanical and Microstructural Properties of Friction Stir Processed Aluminum Alloy Reinforced by Nanoparticles

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