Artificial neural network modelling and multi objective optimisation of hole drilling electro discharge micro machining of invar

Porwal, Rajesh Kumar; Yadava, Vinod; Ramkumar, J.

doi:10.1504/ijmms.2012.049974

Cited by 7 publications

(6 citation statements)

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“…These capabilities of neural network are of primarily significance for modelling of HS-EDMM process. The superiority of using neural networks in modelling of machining processes has been reported in several studies (Tarng et al [12], Dilma et al [13], and Porwal et al [14,15]). The block diagram of modelling of ANN for the present work is shown in fig.…”

Section: Ann Modelling Of Hs-edmm Processmentioning

confidence: 90%

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Modelling of Hole Sinking Electrical Discharge Micro Machining of Ni-Based Super Alloy Thin Sheet

2016

Arimpie - 2016

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Abstract-From the productivity and accuracy point of view fabrication of the symmetrical and nonsymmetrical holes is a challenging task for manufacturing engineers. Creation of these kinds of holes in thin sheets of super alloys can only be possible by using hole sinking electro discharge micro machining (HS-EDMM) process. In the present paper Artificial Neural Network (ANN) model has been proposed for the prediction of material removal rate (MRR) in HS-EDMM. For this purpose Matlab with the neural networks toolbox (nntool) has been used. The neural network based on process model has also been developed to establish relationship between input process conditions (gap voltage and capacitance of the capacitor) and process response material removal rate. The ANN model has been trained and tested using the data generated from an extensive series of experiments on HS-EDMM machine. The trained neural network system has been used to predict MRR for different input conditions. The ANN model has been found to predict accurately HS-EDMM process responses for chosen process conditions. Keywords -Hole sinking electrical discharge micro machining (HS-EDMM), Artificial neural network (ANN), Back-propagation (BP) algorithm.

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Section: Ann Modelling Of Hs-edmm Processmentioning

confidence: 90%

“…Back propagation is a systematic method for training multilayer artificial neural network [14]. It uses gradient-descent method to minimize the total mean square error of the output computed by the network.…”

Section: Ann Modelling Of Hs-edmm Processmentioning

confidence: 99%

Modelling of Hole Sinking Electrical Discharge Micro Machining of Ni-Based Super Alloy Thin Sheet

2016

Arimpie - 2016

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“…For example, optimization of manufacturing processes, [21][22][23][24][25] vibro-acoustic optimization of mechanical structures 29 and optimization of trusses design. 30 To the knowledge of authors, any analytical formulation describing the relationship between the inputs and the outputs of FSP is not available in the literature.…”

Section: Multilayer Neural Network Function Approximationmentioning

confidence: 99%

“…ANN method is more suitable for the applications where there is no way to describe the problem with an analytical function. For example, optimization of manufacturing processes, 21–25 vibro-acoustic optimization of mechanical structures 29 and optimization of trusses design. 30…”

Section: Theoretical Background and The Procedures Of The Optimizationmentioning

confidence: 99%

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Trade-off among mechanical properties and energy consumption in multi-pass friction stir processing of Al7075 alloy employing neural network–based genetic optimization

Hussain

Ranjbar

Hassanzadeh

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Friction stir processing is a novel material processing technique. In this study, neural network-based genetic optimization is applied to optimize the process performance in terms of post-friction stir processing mechanical properties of Al7075 alloy and the energy cost. At first, the experimental data regarding the properties (i.e. elongation, tensile strength and hardness) and the consumed electrical energy are obtained by conducting tests varying two process parameters, namely, feed rate and spindle speed. Then, a numerical model making use of empirical data and artificial neural networks is developed, and multiobjective multivariable genetic optimization is applied to find a trade-off among the performance measures of friction stir processing. For this purpose, the properties like elongation, tensile strength and hardness are maximized and the cost of consumed electrical energy is minimized. Finally, the optimization results are verified by conducting experiments. It is concluded that artificial neural network together with genetic algorithm can be successfully employed to optimize the performance of friction stir processing.

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A rule-based system for trade-off among energy consumption, tool life, and productivity in machining process

et al. 2013

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Artificial neural network modelling and multi objective optimisation of hole drilling electro discharge micro machining of invar

Cited by 7 publications

References 0 publications

Modelling of Hole Sinking Electrical Discharge Micro Machining of Ni-Based Super Alloy Thin Sheet

Modelling of Hole Sinking Electrical Discharge Micro Machining of Ni-Based Super Alloy Thin Sheet

Trade-off among mechanical properties and energy consumption in multi-pass friction stir processing of Al7075 alloy employing neural network–based genetic optimization

A rule-based system for trade-off among energy consumption, tool life, and productivity in machining process

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