Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques

Walia, Arminder Singh; Srivastava, Vineet; Rana, Prashant Singh; Somani, Nalin; Gupta, Nitin Kumar; Singh, Gurminder; Pimenov, Danil Yurievich; Mikołajczyk, Tadeusz; Khanna, Navneet

doi:10.3390/met11111668

Cited by 26 publications

(12 citation statements)

References 48 publications

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“…Machine learning techniques (Walia et al 2021) (CA and FA) were used to optimize the EDM process parameters after identifying the optimum input conditions with Taguchi-PCA based analyses. The prime objective was to increase the MRR and to reduce the RCT and TWR.…”

Section: Resultsmentioning

confidence: 99%

Multiobjective optimization of electric discharge machining of an Al–SiCp composite using the Taguchi–PCA method as well as the firefly and cuckoo search algorithms

S²

2022

Trans. Can. Soc. Mech. Eng.

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Electric Discharge Machining (EDM) processes are extensively utilized in industries for cutting hard to machine materials and geometries that are complex which are not possible with conventional machining. In this research study, efforts are made to identify optimal process parameters of EDM during machining of AA6061-10%SiCp composite material. The novelty of the present work is copper electrode with different geometries such as circular, triangular and square are considered for machining along with input variables discharge current density (A), pulse on and off timing (Ton and Toff) which are varied through three values. The L27 (313) orthogonal array of Taguchi is used for experimental layout and responses measured are recast layer thickness (RCT), electrode tool wear rate (TWR) and material removal rate (MRR). Taguchi’s approach of signal-to-noise (S/N) ratio is integrated with principal component analysis (PCA) for multi-criteria optimization. Also, nature inspired cuckoo search (CS) and firefly algorithm (FA) is employed for identifying the optimal conditions and to predict the outputs for maximum MRR and minimum TWR and RCT. From S/N+PCA analysis the optimal conditions identified are: Circle (12A, 65µs, 2µs), Triangle (12A, 95µs, 6µs) and Square (12A, 65µs, 8µs) was obtained. In all the conditions, discharge current influences higher than the other inputs. Metallurgical examination conducted through micrographs on the machined surface clearly supports the predicted result.

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Section: Resultsmentioning

confidence: 99%

Multiobjective optimization of electric discharge machining of an Al–SiCp composite using the Taguchi–PCA method as well as the firefly and cuckoo search algorithms

S²

2022

Trans. Can. Soc. Mech. Eng.

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“…Based on that, solutions are made to get better machinability of HcHcr with improved material removal rate (MRR), reduced surface roughness (SR), and tool wear rate (TWR). In the investigation conducted by Walia, A.S, et al 31 a distortion of 5.65–37.8 μm has been observed in the tooltip of an EDM process on EN31 tool steel. Investigations have shown that a set of parameters is current mainly involved in the tool shape change.…”

Section: Introductionmentioning

confidence: 96%

“…Based on that, solutions are made to get better machinability of HcHcr with improved material removal rate (MRR), reduced surface roughness (SR), and tool wear rate (TWR). In the investigation conducted by Walia, A.S, et al 31 Research suggests that the use of an optimized setting may improve MRR in the machining process of Al6061-SiC. Arulkirubakaran, D., et al 34 have done Performance analysis of ECM with the hexagonal shaped electrode on SS AISI304.…”

Section: Introductionmentioning

confidence: 99%

Prediction on enhanced electrochemical discharge machining behaviors of zirconia‐silicon nitride using hybridDNNbased spotted hyena optimization

Manoharan

Sekar²

2022

Intl J of Energy Research

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Summary In this work, zirconia composite is machined by the unconventional Electrochemical Discharge Machining (ECDM). Normally, the machining of zirconia is an inconvenient process due to its increased hardness and maximum machining time. The ECDM of zirconia‐silicon nitride is done by varying the input parameters such as electrolytic concentration, voltage, and duty cycle. The measured output machined parameters are material removal rate (MRR), Overcut (OC), and Tool wear rate (TWR). In Response Surface Methodology (RSM), the Box Behnken method is used to plan the experimental design of this work. The parameter optimization is conducted using RSM. Besides, the experimented machining performances are validated using hybrid Deep Neural Network‐based Spotted Hyena optimization (DNN‐SHO) done in MATLAB platform version 2020 a. From the findings, the voltage and electrolytic concentration are identified as signified parameters for improving the ECDM performances from the RSM analysis. The obtained favorable machining performances are 0.371 mg/min of MRR, 162.2 μm of OC, and 0.26 mg/min of TWR. The predicted results from the proposed DNN‐SHO for the MRR are 0.402 mg/min, OC is 152.98 μm, and TWR is 0.21 mg/min. The proposed DNN‐SHO outcomes are in perfect agreement with the experimented values and are more superior to the RSM, DNN, and DNN‐PSO based prediction approaches.

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“…In this method, the combined action of thermal erosion followed by vaporization (by micro-EDM) [4] and electro-chemical etching (by micro-ECM) is responsible for the erosion of materials from the workpiece [5]. Due to the hybridization of the process, a higher rate of material removal (MRR) is attained compared to parent machining processes [6].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Maraging Steel Surface Integrity in Hybrid and Conventional Micro-ECDM Processes

2022

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Maraging steel is one of the exotic materials showing the potential for application in the field of the aerospace industry. However, machining these materials with high surface quality and material removal rate is problematic. The micro-electro chemical discharge (MECDM) process is capable of resolving this problem to some extent, however, due to the spark action, it fails to attain a high surface finish. In the current investigation, micro-hole drilling is performed on maraging steel with powder-mixed alumina (1% wt. of Al2O3) using the micro-electro chemical discharge machining (PMECDM) process. The effect of different input process factors, for example, voltage (V), duty cycle (D), the electrolyte concentration (C), are considered for investigating the machining performance, i.e., rate of material removal (MRR) and roughness of surface (SR) of the machined substrate. Further, a comparative analysis is established between micro-ECDM (MECDM) and mixed powder ECDM (PMECDM). The Box–Behnken design is used to conduct all the experiments and analysis of variance (ANOVA) is used to optimize the results. The outcomes reveal that MRR in PMECDM is enhanced by 34%, and the average surface roughness is reduced by 21% over the MECDM process. The maximum MRR was observed to be 2.44 mg/min and the hole machined by the PMECDM results in a cleaner hole wall surface than the MECDM process due to the grinding action by the powder particles. The residual stress measurement indicates that the PMECDM (−128.3 ± 3.85 MPa) has the lowest equivalent stress as compared to the parent material (−341.04 ± 10.24 MPa) and MECDM (−200.7 ± 6.02 MPa) surfaces. The applied voltage is the most significant parameter, followed by the duty factor and electrolyte concentration for enhancing the MRR and surface finish. The addition of powder improves the surface integrity of the machined surface as compared to the surfaces produced by the MECDM processes.

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Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques

Cited by 26 publications

References 48 publications

Multiobjective optimization of electric discharge machining of an Al–SiCp composite using the Taguchi–PCA method as well as the firefly and cuckoo search algorithms

Multiobjective optimization of electric discharge machining of an Al–SiCp composite using the Taguchi–PCA method as well as the firefly and cuckoo search algorithms

Prediction on enhanced electrochemical discharge machining behaviors of zirconia‐silicon nitride using hybridDNNbased spotted hyena optimization

Comparison of Maraging Steel Surface Integrity in Hybrid and Conventional Micro-ECDM Processes

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