Optimizing Material Removal Rate Using Artificial Neural Network for Micro-EDM

Upadhyay, Ananya; Prakash, Ved; Sharma, Vinay

doi:10.4018/978-1-5225-3401-3.ch011

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…e stainless-steel substance SS630 is extremely hard to do machining due to the factors such as high corrosive resistance, high build-up edge propensity, and poor thermal conductivity. However, the product applications in different industries such as pharmaceutical industries, pump production, and other device prototypes and, in addition, several recent works focused on optimization of process parameters using fuzzy networks [32] and artificial neural networks [33][34][35]. erefore, this article proposes an experimental and thermal investigation with process parameter optimization using BPNN with feed forward architecture on PMEDM for SS630 using SiC powder as dielectric fluid.…”

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confidence: 99%

[Retracted] Experimental and Thermal Investigation on Powder Mixed EDM Using FEM and Artificial Neural Networks

Jampana

Rao

Sampathkumar³

2021

Advances in Materials Science and Engineering

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Electric discharge machining (EDM) process is one of the earliest and most extensively used unconventional machining processes. It is a noncontact machining process that uses a series of electric discharges to remove material from an electrically conductive workpiece. This article is aimed to do a comprehensive experimental and thermal investigation of the EDM, which can predict the machining characteristic and then optimize the output parameters with a newly integrated neural network-based methodology for modelling and optimal selection of process variables involved in powder mixed EDM (PMEDM) process. To compare and investigate the effects caused by powder of differently thermo physical properties on the EDM process performance with each other as well as the pure case, a series of experiments were conducted on a specially designed experimental setup developed in the laboratory. Peak current, pulse period, and source voltage are selected as the independent input parameters to evaluate the process performance in terms of material removal rate (MRR) and surface roughness (Ra). In addition, finite element method (FEM) is utilized for thermal analysis on EDM of stainless-steel 630 (SS630) grade. Further, back propagated neural network (BPNN) with feed forward architecture with analysis of variance (ANOVA) is used to find the best fit and approximate solutions to optimization and search problems. Finally, confirmation test results of experimental MRR are compared using the values of MRR obtained using FEM and ANN. Similarly, the test results of experimental Ra also compared with obtained Ra using ANN.

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confidence: 99%

[Retracted] Experimental and Thermal Investigation on Powder Mixed EDM Using FEM and Artificial Neural Networks

Jampana

Rao

Sampathkumar³

2021

Advances in Materials Science and Engineering

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“…Sidhu et al [ 23 ] used ANN to predict residual stress during EDM of Al/SiC metal matrix composites after finding out the significant factors by the analysis of variance(ANOVA) method. Upadhyay et al [ 24 ] attempted to directly use ANN model to find the significant factors which impacted the MRR of the micro-EDM process with additives in the dielectric fluid. Sagbas et al [ 25 ] combined Taugchi method and BPNN model to effectively help engineers to determine the optimum process parameters during EDM.…”

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confidence: 99%

Spark Analysis Based on the CNN-GRU Model for WEDM Process

et al. 2021

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Wire electrical discharge machining (WEDM), widely used to fabricate micro and precision parts in manufacturing industry, is a nontraditional machining method using discharge energy which is transformed into thermal energy to efficiently remove materials. A great amount of research has been conducted based on pulse characteristics. However, the spark image-based approach has little research reported. This paper proposes a discharge spark image-based approach. A model is introduced to predict the discharge status using spark image features through a synchronous high-speed image and waveform acquisition system. First, the relationship between the spark image features (e.g., area, energy, energy density, distribution, etc.) and discharge status is explored by a set of experiments). Traditional methods have claimed that pulse waveform of “short” status is related to the status of non-machining while through our research, it is concluded that this is not always true by conducting experiments based on the spark images. Second, a deep learning model based on Convolution neural network (CNN) and Gated recurrent unit (GRU) is proposed to predict the discharge status. A time series of spark image features extracted by CNN form a 3D feature space is used to predict the discharge status through GRU. Moreover, a quantitative labeling method of machining state is proposed to improve the stability of the model. Due the effective features and the quantitative labeling method, the proposed approach achieves better predict result comparing with the single GRU model.

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