Dry sliding wear characteristics evaluation and prediction of vacuum casted marble dust (MD) reinforced ZA-27 alloy composites using hybrid improved bat algorithm and ANN

Gangwar, Swati; Pathak, Vimal Kumar

doi:10.1016/j.mtcomm.2020.101615

Cited by 37 publications

(23 citation statements)

References 32 publications

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“…Gangwar and Pathak [62] introduced a novel improved bat algorithm (IBA) to train an ANN for predicting the wear behavior of marble dust reinforced zinc-aluminum (Zn-Al) alloy by optimizing the weights, biases and neurons as well as finding minimum mean squared errors, see Figure 5a). The main advantage of the IBA compared to other training algorithms (e.g., back propagation, genetic algorithms or particle swarm optimization) was in the flexibility and stable training through the introduction of a new velocity, position search equation and sugeno inertia weights.…”

Section: Metal Matrix Compositesmentioning

confidence: 99%

Current Trends and Applications of Machine Learning in Tribology—A Review

2021

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Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.

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Section: Metal Matrix Compositesmentioning

confidence: 99%

Current Trends and Applications of Machine Learning in Tribology—A Review

2021

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“…us they are widely used for solving optimization problems, such as mission planning [4][5][6][7], image segmentation [8][9][10], feature selection [11][12][13], and parameter optimization [14][15][16][17][18]. Metaheuristic algorithms find optimal solutions by modeling physical phenomena or biological activities in nature.…”

Section: Introductionmentioning

confidence: 99%

“…Metaheuristic optimization algorithms are able to obtain optimal or near-optimal solutions within a reasonable amount of time [ 3 ]. Thus they are widely used for solving optimization problems, such as mission planning [ 4 – 7 ], image segmentation [ 8 – 10 ], feature selection [ 11 – 13 ], and parameter optimization [ 14 – 18 ]. Metaheuristic algorithms find optimal solutions by modeling physical phenomena or biological activities in nature.…”

Section: Introductionmentioning

confidence: 99%

A Modified Slime Mould Algorithm for Global Optimization

Tang

Han

et al. 2021

Computational Intelligence and Neuroscience

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Slime mould algorithm (SMA) is a population-based metaheuristic algorithm inspired by the phenomenon of slime mould oscillation. The SMA is competitive compared to other algorithms but still suffers from the disadvantages of unbalanced exploitation and exploration and is easy to fall into local optima. To address these shortcomings, an improved variant of SMA named MSMA is proposed in this paper. Firstly, a chaotic opposition-based learning strategy is used to enhance population diversity. Secondly, two adaptive parameter control strategies are proposed to balance exploitation and exploration. Finally, a spiral search strategy is used to help SMA get rid of local optimum. The superiority of MSMA is verified in 13 multidimensional test functions and 10 fixed-dimensional test functions. In addition, two engineering optimization problems are used to verify the potential of MSMA to solve real-world optimization problems. The simulation results show that the proposed MSMA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.

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Section: Introductionmentioning

confidence: 99%

“…The prediction of wear characteristics of zinc-aluminum composites using artificial neural network and Taguchi analysis was carried out where the reinforcement content was found to have highest impact on the wear response. 27 In this research, an attempt is made to model the sliding wear phenomenon of Al5052/TiC MMCs using a regression-based RSM and Mamdani model-based fuzzy logic system as shown in Supplemental Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Multi-response modeling for sliding wear behavior of AA5052/TiC composites by stir casting: A comparative analysis using response surface methodology and fuzzy logic system

Samal

Vundavilli

Meher

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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The present research deals with the modeling of wear performance of AA5052 metal matrix composites (MMCs) reinforced by in-situ formed TiC particles using two methods, namely statistical-based non-linear regression and fuzzy logic system. A pin-on-disc apparatus is employed to obtain the wear data of the composites. Three different process parameters, namely weight percentage of TiC, sliding distance, and applied load are considered as the input variables, and the volumetric wear loss and coefficient of friction are taken as output variables of the wear process. Regression models are formulated to study the output responses based on the wear results. To check the significance and contribution of the various control parameters, analysis of variance method was performed. Further, confirmatory tests were also conducted to validate both the models. It has been observed that the sliding distance and applied load have shown a substantial effect on the volumetric wear loss. It is also observed that the coefficient of friction is found to be increased with the rise in the sliding distance. The novelty of the work lies in using fuzzy logic to establish the wear model for aluminum 5052 MMC. A comparative study is performed to analyze the prediction accuracy of the models for the wear characteristics of the aluminum composites. Consequently, this wear modeling investigation will give a better understanding the effect of parameters and the responses influence of wear phenomena of Al 5052/TiC composites, which further will help in developing wear components for industrial applications.

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Dry sliding wear characteristics evaluation and prediction of vacuum casted marble dust (MD) reinforced ZA-27 alloy composites using hybrid improved bat algorithm and ANN

Cited by 37 publications

References 32 publications

Current Trends and Applications of Machine Learning in Tribology—A Review

Current Trends and Applications of Machine Learning in Tribology—A Review

A Modified Slime Mould Algorithm for Global Optimization

Multi-response modeling for sliding wear behavior of AA5052/TiC composites by stir casting: A comparative analysis using response surface methodology and fuzzy logic system

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