Prediction of surface roughness in magnetic abrasive finishing using acoustic emission and force sensor data fusion

Oh, Jeong-Ik; Lee, S H

doi:10.1177/09544054jem2055

Cited by 29 publications

(17 citation statements)

References 27 publications

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“…In 1998, Williams 103 developed an Acoustic emission (AE)-based monitoring approach for monitoring the performance of the AFM process. During MAF process, Oh and Lee 104 observed the AE signals and force signals to estimate surface roughness of finishing profile. They used the AE signals as input parameters of ANNs for monitoring and prediction of nanoscale precision finishing processes.…”

Section: Future Research Scopementioning

confidence: 99%

Recent developments in abrasive flow finishing process: A review of current research and future prospects

Sambharia

Mali

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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The abrasive flow finishing processes are advanced finishing techniques widely used as finishing solutions in high-end industries like aerospace, automobile, tool and die, and prosthetic. Abrasive flow finishing processes are mainly used for polishing, deburring, removal of cast layers, radiusing and finishing intricate shapes by flowing abrasive laden viscoelastic carrier over the surfaces to be finished. Although abrasive flow finishing have a wide range of applications and could be used in every shop floor as a finishing solution, their usage is limited owing to high initial and running costs associated with them. This article attempts to present a novel classification of abrasive flow finishing processes based upon the use of different energy and tooling, and highlights the critical research outcomes in each category. The physical abrasive flow finishing modeling, developments in abrasive flow finishing processes, and their hybridization with the other manufacturing processes are discussed. Finally, the future research challenges including the development of low-cost alternative media, development of tooling and fixture, and environmental issues in the area of abrasive flow finishing are highlighted.

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Section: Future Research Scopementioning

confidence: 99%

Recent developments in abrasive flow finishing process: A review of current research and future prospects

Sambharia

Mali

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Oh and Lee in their study applied ANN-proposed sensor (fusion) scheme for the monitoring and prediction of nanoscale precision finishing processes using MAF process. Among the three networks proposed, the ANN-based network provides best outcome in terms of surface roughness [30]. The ANN generally uses back-propagation algorithm for training, making it proficient to model complex nonlinear functions and interactions which are far more accurate in comparison with the linear and exponential regression models.…”

Section: Introductionmentioning

confidence: 99%

A novel hybridization of artificial neural network and moth-flame optimization (ANN–MFO) for multi-objective optimization in magnetic abrasive finishing of aluminium 6060

Singh

Gangwar

Singh

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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In industries, the impact of magnetic abrasive finishing (MAF) is well recognized in achieving accurate surfaces, minimizing imperfections and providing high-quality surface finish especially in micro-and nano-range. In the same context, this paper presents a hybrid optimization method for multi-objective optimization that combines back-propagation artificial neural network (ANN) with a newly developed nature-inspired optimization algorithm, i.e. moth-flame optimization (MFO) algorithm, which is used to predict optimal process parameters of magnetic abrasive finishing process. The back-propagation ANN is gradient-based local search algorithm, while MFO is a rapid and global search algorithm for controlling the generation of candidate solutions. Initially, the MFO algorithm is integrated for training the neural network with unknown set of weights and biases and in the process overcomes the shortcomings of traditional training algorithms having slow convergence and local optima stagnation. Further, the MFO algorithm is also employed to predict the number of neurons in hidden layers. Finally, the proposed hybrid ANN-MFO methodology establishes a multi-objective optimization model in terms of important process parameters for optimizing the MAF process conditions. The optimization of different MAF process parameters is performed using MFO algorithm by incorporating the adaptive search procedure, which further enhances the exploration behaviour. Surface roughness, temperature of workpiece during the finishing operation and hardness of finished surface are investigated as the output response in the finishing of aluminium 6060. The design variables considered are working gap, abrasive weight, voltage and rotational speed. The results predicted by the proposed hybrid ANN-MFO algorithm provide effective and accurate process parameters to enhance surface finish and part quality that will be beneficial for real manufacturing environment.

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“…Therefore, the research on surface roughness in polishing has become a hot topic in the field of engineering and academic. 3,4 The polishing process is a complex material removal process involving rubbing, scratching, plowing and cutting between the workpiece and a large number of grains on the surface of the polishing tool. Therefore, there are a number of factors in the polishing process that can affect the surface roughness, 5 including process parameters such as the tool speed, the feed rate and the polishing force; the characteristics of the polishing tool such as the grain size and material as well as the characteristics of the workpiece such as modulus and curvature.…”

Section: Introductionmentioning

confidence: 99%

Modeling and prediction of surface roughness in belt polishing based on artificial neural network

Zhang

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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Surface roughness is a variable often used to describe the quality of ground surfaces as well as to evaluate the competitiveness of the overall polishing system, which makes it an ever-increasing concern in industries and academia nowadays. In this article, from microscopic point of view, based on the statistics analysis, and by the use of the elastic contact theory and the plastic contact theory, the model of the maximum cutting depth of abrasive grains is developed. Then based on back-propagation neural network, taking the maximum cutting depth of abrasive grains, the rotation speed of belt and the feed rate of workpiece as the input parameters, a prediction model of surface roughness in belt polishing is presented. The prediction model fully takes the characteristics of polishing tool and workpiece into consideration which makes the model more comprehensive. Compared with the model that takes the polishing force as the input parameter, the model in this article needs fewer experiment samples which will save the experiment cost and time. Moreover, it has a wider range of uses and is suitable for different polishing situations such as different workpieces and polishing tools. The results indicate a good agreement between the predicted values and experimental values which verify the model.

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Prediction of surface roughness in magnetic abrasive finishing using acoustic emission and force sensor data fusion

Cited by 29 publications

References 27 publications

Recent developments in abrasive flow finishing process: A review of current research and future prospects

Recent developments in abrasive flow finishing process: A review of current research and future prospects

A novel hybridization of artificial neural network and moth-flame optimization (ANN–MFO) for multi-objective optimization in magnetic abrasive finishing of aluminium 6060

Modeling and prediction of surface roughness in belt polishing based on artificial neural network

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