Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

Kathiresan, S.; Hariharan, K.; Mohan, B.

doi:10.4028/www.scientific.net/amm.766-767.1076

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Turning experiments have been designed by using the three-level full factorial statistically based experimental design techniques. Kathiresan et al [27] predicted the surface roughness of stainless steel (SS-304) in magneto-rheological abrasive flow finishing process using ANN and regression models. A feed-forward NN has been implemented using back-propagation consisting of 27 inputs, 7 hidden, and one output neuron.…”

Section: State Of the Art In Artificial Neural Network (Ann)mentioning

confidence: 99%

Neural Simulation of Surface Generated During Magnetic Abrasive Flow Machining of Hybrid Al/SiC/B4C-MMCs

Chawla

Kumar

Sharma³

2021

J Bio Tribo Corros

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Modern manufacturing processes like abrasive flow machining (AFM) are often employed for finishing the conventionally inaccessible surfaces that include the inner surfaces of cylindrical workpieces. This process is used for accurate prediction of the generated surfaces. As such, regression models are useful for such a prediction. In the existing AFM setup, the nylon fixture is replaced by an aluminum fixture, and a modified MAFM setup is fabricated by including the effect of magnetization. The research explores the MAFM process for finishing the hybrid metal matrix composites (MMCs) of SiC/B 4 C using aluminum-6063 as a base material. The paper employs artificial neural networks to model and simulate the response characteristics during the MAFM process, developed in the MATLAB 2016 b environment. The neural networks are trained for finishing the cylindrical components of Al/SiC/B 4 C-MMCs. A neural network having a configuration of generalized backpropagation is employed with six inputs, two outputs, and two hidden layers. The experimentally observed data take into account the influence of MAFM process parameters including the number of cycles, extrusion pressure, the concentration of abrasives, magnetic flux density, mesh size, and workpiece material. The process responses are characterized as material removal rate (MRR; μg/s) and change in surface roughness (ΔR a ; μm). Moreover, the microstructure analysis of workpiece materials is also done using scanning electron microscope.

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Section: State Of the Art In Artificial Neural Network (Ann)mentioning

confidence: 99%

Neural Simulation of Surface Generated During Magnetic Abrasive Flow Machining of Hybrid Al/SiC/B4C-MMCs

Chawla

Kumar

Sharma³

2021

J Bio Tribo Corros

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“…Magnetorheological fnishing (MRF) [9], magnetic foat polishing (MFP) [10], magnetic abrasive fnishing (MAF) [11], and AFM [12] are advanced fne fnishing processes that aim to control the abrading forces. Although the magnetic feld is used to modulate the abrading forces in MAF, MRF, and MFP, their applications are limited [13].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigations of Nano Finishing Process on Nickel-Free Austenitic Stainless Steel by Grey Relational and Principal Component Analysis

Kathiresan

Anbuchezhiyan

Karthikeyan

et al. 2023

Advances in Materials Science and Engineering

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The nano surface roughness of metallic materials is important in engineering and medical fields for specific applications. Magneto rheological abrasive flow finishing (MRAFF) process was performed on nickel-free austenitic stainless workpieces in order to obtain surface roughness at the nano level and also to forecast the performance of the MRAFF process in terms of responses such as surface roughness (SR) and material removal rate (MRR). These two responses are affected by process factors such as hydraulic pressure, current to the electromagnet, and the number of cycles performed during the machining process. The design of experiments (DOE) was used to determine the contributions of process parameters to output responses. The techniques of grey relational analysis (GRA) and principal component analysis (PCA) were used in these experimental investigations to discover the process factors that minimise the final Ra and maximise MRR. Through the DOE, a minimum SR of 63.24 nm and a maximum MRR of 2.34 mg/sec were obtained on the samples for the combination of 30 bar pressure, 6 A current, and 300 number of cycles.

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Experimental investigation and optimization of process parameters of hybrid Al/SiC/B4C–MMCs finished by MAFM process using RSM modeling with supervised machine learning algorithm

Chawla

Mittal

2023

Sādhanā

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Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

Cited by 4 publications

References 24 publications

Neural Simulation of Surface Generated During Magnetic Abrasive Flow Machining of Hybrid Al/SiC/B4C-MMCs

Neural Simulation of Surface Generated During Magnetic Abrasive Flow Machining of Hybrid Al/SiC/B4C-MMCs

Experimental Investigations of Nano Finishing Process on Nickel-Free Austenitic Stainless Steel by Grey Relational and Principal Component Analysis

Experimental investigation and optimization of process parameters of hybrid Al/SiC/B4C–MMCs finished by MAFM process using RSM modeling with supervised machine learning algorithm

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