An integrated ANN – PSO approach to optimize the material removal rate and surface roughness of wire cut EDM on INCONEL 750

Babu, K. Naveen; Karthikeyan, R.; Punitha, A.

doi:10.1016/j.matpr.2019.07.643

Cited by 28 publications

(17 citation statements)

References 11 publications

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“…Furthermore, 10 input/ target pairs for evading overfitting and attaining good generalization by means of cross-validation. Some test runs (2,4,7,11,14,19,22,23,27, 31 and 34) has been Collected for validating (testing) of developed model.…”

Section: Calculation Of Optimum Machining Variablesmentioning

confidence: 99%

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Optimization of backpropagation neural network-based models in EDM process using particle swarm optimization and simulated annealing algorithms

Saffaran

Moghaddam

Kolahan

2020

J Braz. Soc. Mech. Sci. Eng.

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In the present study, artificial neural network (ANN) along with heuristic algorithms, namely particle swarm optimization (PSO) and simulated annealing (SA), has been employed to carry out the modeling and optimization procedure of electrical discharge machining (EDM) process on AISI2312 hot worked steel parts. Surface roughness (SR), tool wear rate (TWR) and material removal rate (MRR) are the process quality measures considered as process output characteristics. Determination of a process variables (pulse on and off time, current, voltage and duty factor) combination to minimize TWR and SR and maximize MRR independently (as single objective) and also simultaneously (as multi-criteria) optimization is the main objective of this study. The experimental data are gathered using Taguchi L 36 orthogonal array based on design of experiments approach. Next, the output measures are used to develop the ANN model. Furthermore, the architecture of the ANN has been modified using PSO algorithm. At the last step, in order to determine the best set of process output variables values for a desired set of process quality measures, the developed ANN model is embedded into proposed heuristic algorithms (SA and PSO) with which their derived results have been compared. It is evident that the proposed optimization procedure is quite efficient in modeling (with less than 1% error) and optimization (less than 4 and 7 percent error for single-and multiobjective optimizations, respectively) of EDM process variables.

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Section: Calculation Of Optimum Machining Variablesmentioning

confidence: 99%

“…Along this line, Naveen et al [11] have employed orthogonal array Taguchi method to get the optimized MRR and SR of WEDM on Inconel 750 considering process input Graphic illustration of electrical discharge machining process [2] variables (pulse on, pulse off, voltage and current). To model the MRR and SR, an ANN model has been developed.…”

Section: Introductionmentioning

confidence: 99%

Optimization of backpropagation neural network-based models in EDM process using particle swarm optimization and simulated annealing algorithms

Saffaran

Moghaddam

Kolahan

2020

J Braz. Soc. Mech. Sci. Eng.

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“…EDM process variables were modeled by using artificial neural networks (ANNs) and ANFIS, as shown in studies such as that of Rahul et al [32], where the authors employed a Taguchy design of experiments, as well as the concept of satisfaction function, to improve machining performances responses in EDM of Inconel 718. Babu et al [33] employed a Taguchy design of experiments and an ANN in order to determine optimal parameters in the wire electrical discharge machining (WEDM) of Inconel 750. Likewise, Al-Ghamdi et al employed an adaptive neuro-fuzzy inference system (ANFIS) and polynomial modeling approaches to model the material removal rate in EDM of a Ti-6Al-4V alloy [34].…”

Section: State Of the Artmentioning

confidence: 99%

Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Pérez

2020

Mathematics

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Technological tables are very important in electrical discharge machining to determine optimal operating conditions for process variables, such as material removal rate or electrode wear. Their determination is of great industrial importance and their experimental determination is very important because they allow the most appropriate operating conditions to be selected beforehand. These technological tables are usually employed for electrical discharge machining of steel, but their number is significantly less in the case of other materials. In this present research study, a methodology based on using a fuzzy inference system to obtain these technological tables is shown with the aim of being able to select the most appropriate manufacturing conditions in advance. In addition, a study of the results obtained using a fuzzy inference system for modeling the behavior of electrical discharge machining parameters is shown. These results are compared to those obtained from response surface methodology. Furthermore, it is demonstrated that the fuzzy system can provide a high degree of precision and, therefore, it can be used to determine the influence of these machining parameters on technological variables, such as roughness, electrode wear, or material removal rate, more efficiently than other techniques.

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“…The MRR and SR of Inconel 750 in the WEDM process have been optimized with reasonable accuracy using the supervised learning and optimization (SLO) method, which adopts an integrated ANN-particle swarm optimization (PSO) methodology. 9 Similar to the nickel-based alloys discussed above, many SLO methodologies are applied to optimize the WEDM performance of titanium and stainless-steel alloys. Yusoff et al 10 applied the ANN-GA SLO methodology to optimize the WEDM performance of Ti-48Al intermetallic alloys and observed that the 5-6-6-4 feed-forward back propagation neural network (FFNN) is the most precise architecture with very good prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

Biswas,

Singh,

Mukherjee

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In the field of machining different grades of alloys using wire electrical discharge machining (WEDM), developing efficient techniques has been a challenge. Typically, specific supervised learning and optimization (SLO) methods are created for individual alloys, resulting in a time-consuming process. To address this issue, a unified SLO approach capable of handling various grades of alloys with different compositions and physical characteristics is proposed. In this study, three stochastic optimization tools are combined with a multi-layer neural network to determine the optimal SLO strategy. Five different alloy grades are considered, with inputs consisting of six variables, including chemical composition, and physical properties. The neural network architecture used is 19-500-500-4, and it is combined with a gray wolf stochastic algorithm, proving to be the most effective SLO methodology. The results indicate that regardless of the alloy grade, the pulse on time significantly affects the material removal rate, kerf width, and thickness of the recast layer (RL). Surface roughness is primarily influenced by alloy properties such as density, specific heat, and elements like Cr, Mo, and Ni. Additionally, the recast layer is dependent on factors such as V, Ti, thermal conductivity, and thermal diffusivity of the materials. Validation experiments demonstrate that the mean squared error of all responses for each alloy remains within 5.0%. This unified SLO approach provides a promising solution for optimizing the machining process of different alloy grades, reducing the need for separate techniques for each material.

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An integrated ANN – PSO approach to optimize the material removal rate and surface roughness of wire cut EDM on INCONEL 750

Cited by 28 publications

References 11 publications

Optimization of backpropagation neural network-based models in EDM process using particle swarm optimization and simulated annealing algorithms

Optimization of backpropagation neural network-based models in EDM process using particle swarm optimization and simulated annealing algorithms

Using a Fuzzy Inference System to Obtain Technological Tables for Electrical Discharge Machining Processes

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

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