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

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Abstract

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/B4C 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/B4C-MMCs. A neural network having a configuration of generalized back-propagation 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 (ΔRa; μm). Moreover, the microstructure analysis of workpiece materials is also done using scanning electron microscope.

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Data availability

The data that support the findings of this study are available on request from the corresponding author, [initials]. The data are not publicly available due to [restrictions. e.g., their containing information that could compromise the privacy of research participants].

Abbreviations

AFM:

Abrasive flow machining

MAFM:

Magnetic abrasive flow machining

ANN:

Artificial neural networks

MMCs:

Metal matrix composites

SEM:

Scanning electron microscope

MSE:

Mean square error

MRR:

Material removal rate

RSM:

Response surface methodology

BBD:

Box-Behnken design

ΔR a :

Change in surface roughness

trainbr:

Bayesian regularization training algorithm

lr:

Learning rate of ANN

GA:

Genetic algorithm

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Acknowledgements

The authors would like to thank the National Institute of Technology, Kurukshetra, Haryana, India, for providing the necessary facilities to fabricate the MAFM setup for experimentation.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Kurukshetra, Haryana, 136119, India

    Gagandeep Chawla & Vinod Kumar

  2. Department of Electrical Engineering, JMIT Radaur, Yamunanagar, Haryana, 135001, India

    Rishi Sarup Sharma

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  1. Gagandeep Chawla
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  2. Vinod Kumar
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  3. Rishi Sarup Sharma
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GC: Contributed substantially to the conception and design of the study, the acquisition of data, or the analysis and interpretation. VK: Drafted or provided critical revision of the article. RSS: Agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or the integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Gagandeep Chawla.

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Chawla, G., Kumar, V. & Sharma, R.S. Neural Simulation of Surface Generated During Magnetic Abrasive Flow Machining of Hybrid Al/SiC/B4C-MMCs. J Bio Tribo Corros 7, 153 (2021). https://doi.org/10.1007/s40735-021-00587-4

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