Three-dimensional numerical simulation of microelectric discharge machining of Ti-6Al-4V

Kuriachen, Basil; Varghese, Alwin; Somashekhar, K. P.; Panda, Satyananda; Mathew, Jose

doi:10.1007/s00170-015-6794-y

Cited by 36 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A 3D FVM method is provided as another addition to the modelling and simulation of the sophisticated µ-EDM operation. The results of the generated model were found to be quite close to the experimental results for crater size and temperature distribution [4]. A new meshless method named Meshless Local Petro-Galerkin (MLPG) and Element Free Galerkin Method (EFGM) is applied for solving for solving electrostatic problems.…”

Section: Introductionsupporting

confidence: 53%

Numerical Modelling and Simulation of μ-EDM Process on Inconel 718 Using FEM Model

Dutta

Sarma

Das

et al. 2022

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

Nickle based super alloy Inconel 718 is a difficult to cut material due to its large mechanical strength and high heat and corrosion resistance with good thermal stability. μ-EDM is a non-conventional micro-machining process that can produce complex micro features in Inconel 718. The material removal mechanism of μ-EDM is extremely intricate and dynamic in nature. This paper aims to develop a 2D axisymmetric heat transient model that can explain the material removal theory during the single spark μ-EDM process on Inconel 718. The solutions of the developed model have been obtained by finite element method (FEM) based software COMSOL Multiphysics 5.6. First, the model is validated with a literature model by incorporating the same machining condition. It has been accomplished that the present model’s crater radius (r′) and depth (h) values are in a close agreement with the literature model. The absolute percentage error for r′ and h between the simulated results of the two models are found to be 1.37 and 4.45 correspondingly. Both the r′ and h are increased with the enhancement of discharge energy (DE). The highest value of r′ and h are found at 7.2 μJ DE followed by 6.05 μJ and 5 μJ DE. It has been established that the temperature has been drastically decreased along both the radius and depth direction after the single spark simulation process. The temperature gradient is found to be more along the radius direction than the depth direction.

show abstract

Section: Introductionsupporting

confidence: 53%

Numerical Modelling and Simulation of μ-EDM Process on Inconel 718 Using FEM Model

Dutta

Sarma

Das

et al. 2022

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…The parametric sensitivity of the SL architecture’s connection weights 38,39 on the four responses was measured and depicted in Figure 7. The results indicate that SV and WF have greater sensitivity compared to other process parameters (Figure 7(a)).…”

Section: Resultsmentioning

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:

View full text Add to dashboard Cite

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.

show abstract

“…This solution was not available in the simulation; therefore, a constant speed was applied to the electrode. In the contact area between the elements, the heat exchange coefficient was imposed equal to 1000 W/m 2 K, as suggested by the literature [22]. All the workpiece surfaces were considered adiabatic, except for the area in which the heat flux is applied.…”

Section: Fem Modelmentioning

confidence: 99%

A Comparison between Finite Element Model (FEM) Simulation and an Integrated Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) Approach to Forecast Performances of Micro Electro Discharge Machining (Micro-EDM) Drilling

et al. 2021

View full text Add to dashboard Cite

Artificial Neural Network (ANN), together with a Particle Swarm Optimization (PSO) and Finite Element Model (FEM), was used to forecast the process performances for the Micro Electrical Discharge Machining (micro-EDM) drilling process. The integrated ANN-PSO methodology has a double direction functionality, responding to different industrial needs. It allows to optimize the process parameters as a function of the required performances and, at the same time, it allows to forecast the process performances fixing the process parameters. The functionality is strictly related to the input and/or output fixed in the model. The FEM model was based on the capacity of modeling the removal process through the mesh element deletion, simulating electrical discharges through a proper heat-flux. This paper compares these prevision models, relating the expected results with the experimental data. In general, the results show that the integrated ANN-PSO methodology is more accurate in the performance previsions. Furthermore, the ANN-PSO model is faster and easier to apply, but it requires a large amount of historical data for the ANN training. On the contrary, the FEM is more complex to set up, since many physical and thermal characteristics of the materials are necessary, and a great deal of time is required for a single simulation.

show abstract

Three-dimensional numerical simulation of microelectric discharge machining of Ti-6Al-4V

Cited by 36 publications

References 34 publications

Numerical Modelling and Simulation of μ-EDM Process on Inconel 718 Using FEM Model

Numerical Modelling and Simulation of μ-EDM Process on Inconel 718 Using FEM Model

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

A Comparison between Finite Element Model (FEM) Simulation and an Integrated Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO) Approach to Forecast Performances of Micro Electro Discharge Machining (Micro-EDM) Drilling

Contact Info

Product

Resources

About