Modelling and optimisation of magnetic abrasive finishing process based on a non-orthogonal array with ANN-GA approach

Ahmad, Shadab; Singari, Ranganath M.; Mishra, R. S.

doi:10.1080/00202967.2020.1776966

Cited by 37 publications

(14 citation statements)

References 43 publications

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“…Since the tool pin profile is a categorical variable (Bartholomew, 2013), one-hot encoding was done. An ANN, with one hidden layer of 10 neurons, was trained with the Levenberg-Marquardt optimization algorithm (Ahmad et al, 2020). The L-M network training function, the hyperbolic tangent sigmoid as the transfer function for hidden and output layers.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Bector

Butola²,

Singari³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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This research focuses on the use of Artificial Neural Network (ANN) for the prediction of the microhardness of friction stir processed aluminium based metal matrix composite (AA6061+Al2O3). Different specimens were obtained by using rotating speeds of 1100, 1210, 1320 and 1430 rpm and travelling speeds of 36, 48, 60, 72 mm/min. The microhardness value (HV) of the processed surface of each of the samples was measured and the data collected from the specimens was used as learning data for ANN. Higher rotational speed and lower transversal speeds resulted in higher hardness value since processing at higher tool rotational speed causes high material flow and good resistance to the tool pin profile. A uniform increase in microhardness was observed up to 1320 RPM and a subsequent decrease on any further increments of tool rotational speed. Subsequently, the highest values of microhardness were observed with a square mandrel at 1320 RPM and 36 mm/min. The calculated results were satisfactorily compliant with the measured data and the ANN model was successful in predicting the microhardness.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Bector

Butola²,

Singari³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

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“…In the current manufacturing field, the latest research shows a trend away from traditional finishing methods towards advanced capabilities to achieve high surface finishing performance for micro-sized components having a complex shape. Magnetic energy-assisted finishing processes have proven effective for ultra-smooth surface among the advanced finishing technologies in the last few decades, where multiple cutting edges of tools with controllable magnetic force are able to obtain a high degree of micro/nano surface finishing performance [4,5]. Numerous studies of magnetic surface finish have been extensively conducted from diverse points of view to develop process efficiency and establish a predictive model representing a relationship between process parameters and surface roughness improvement.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction and optimization in the REMF process using an integrated DBN-GA approach

Lee

Seo

Kwak

2022

Int J Adv Manuf Technol

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Surface roughness is a crucial factor affecting the surface quality of workpieces in the manufacturing industries. Thus, it is important to provide an accurate performance of surface roughness prediction and optimal parameters to reduce the burden of time and costs during the process. In this study, two predict models, namely multiple linear regression and deep belief network(DBN) models, were performed to accurately predict change in surface roughness in the rotational-electro magnetic finishing(REMF). Compared to the statistical-based model, the data-driven model based on the DBN architecture was a significantly considerable effect on surface roughness prediction in the REMF process. Among the considered DBN models, DBN5 architecture as [7,14,14,1] showed effective features of the non-linear relationship between process parameters and response with the highest determination coefficient(R 2 ) of 0.9340 and the lowest mean squared error(MSE) of 1.3037×10 -3 in the testing datasets. In addition, a genetic algorithm(GA) as a heuristic optimization technique was adopted to optimize the input parameters of the best derived DBN model. As a result, it proved that the DBN model integrated GA was able to be adopted for the accurate prediction of surface roughness and process optimization.

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“…It should be noted that AFM is found under a variety of process-assisted alternatives such as ultrasonic-assisted AFM [5], magnetic abrasivesassisted AFM [6], and electrochemical-aided abrasive flow finishing (ECA2FM) [7] to name a few. Other important advances in abrasive machining techniques forming a research perspective are presented in references [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Abrasive Flow Nano-Finishing Processes by Adopting Artificial Viral Intelligence

Fountas

Vaxevanidis

2021

JMMP

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This work deals with the optimization of crucial process parameters related to the abrasive flow machining applications at micro/nano-levels. The optimal combination of abrasive flow machining parameters for nano-finishing has been determined by applying a modified virus-evolutionary genetic algorithm. This algorithm implements two populations: One comprising the hosts and one comprising the viruses. Viruses act as information carriers and thus they contribute to the algorithm by boosting efficient schemata in binary coding to facilitate both the arrival at global optimal solutions and rapid convergence speed. Three cases related to abrasive flow machining have been selected from the literature to implement the algorithm, and the results corresponding to them have been compared to those available by the selected contributions. It has been verified that the results obtained by the virus-evolutionary genetic algorithm are not only practically viable, but far more promising compared to others as well. The three cases selected are the traditional “abrasive flow finishing,” the “rotating workpiece” abrasive flow finishing, and the “rotational-magnetorheological” abrasive flow finishing.

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Modelling and optimisation of magnetic abrasive finishing process based on a non-orthogonal array with ANN-GA approach

Cited by 37 publications

References 43 publications

Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Prediction of Hardness in Friction Stir Processing by Artificial Neural Networks

Surface roughness prediction and optimization in the REMF process using an integrated DBN-GA approach

Optimization of Abrasive Flow Nano-Finishing Processes by Adopting Artificial Viral Intelligence

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