A novel approach for modeling MRR in EDM process using utilized discharge energy

Sahoo, Rajesh; Singh, Nirmal; Bajpai, Vivek K.

doi:10.1016/j.ymssp.2022.109811

Cited by 17 publications

(5 citation statements)

References 75 publications

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“…Taking into account the impact of pulse parameters and measurement errors, Equation ( 8) is modified or deformed to derive Equation (10).…”

Section: Physical Principles and Data-driven Hybrid Modelsmentioning

confidence: 99%

“…In this model, the combination of the Backpropagation Artificial Neural Network (BP-ANN) algorithm and the genetic algorithm is employed to predict the processing rate. The process coefficient K process in Equation (10) establishes the connection between input parameters like voltage and current pulse parameters and the process coefficient K process through the BP-ANN algorithm. Simultaneously, the genetic algorithm is used to search for optimal values of variable parameters a, b, and c on a global scale.…”

Section: Physical Principles and Data-driven Hybrid Modelsmentioning

confidence: 99%

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A Novel Physically Guided Data Fusion Prediction Model for Micro-EDM Drilling

Cheng,

Liu,

Cheng

et al. 2023

Materials

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Accurate prediction of Electro-Discharge Machining (EDM) results is crucial for industrial applications, aiming to achieve high-performance and cost-efficient machining. However, both the current physical model and the standard Artificial Neural Network (ANN) model exhibit inherent limitations, failing to fully meet the accurate requirements for predicting EDM machining results. In addition, Micro-EDM Drilling can lead to the distortion of the macroscopic shape of machining pits under different input conditions, rendering the use of only the volume of machining pits as the evaluation index insufficient to express the complete morphological information. In this study, we propose a novel hybrid prediction model that combines the strengths of both physical and data-driven models to simultaneously predict Material Removal Rate (MRR) and shape parameters. Our experiment demonstrates that the hybrid model achieves a maximum prediction error of 4.92% for MRR and 5.28% for shape parameters, showcasing excellent prediction accuracy and stability compared to the physical model and the standard ANN model.

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“…Taking into account the impact of pulse parameters and measurement errors, Equation ( 8) is modified or deformed to derive Equation (10).…”

Section: Physical Principles and Data-driven Hybrid Modelsmentioning

confidence: 99%

Section: Physical Principles and Data-driven Hybrid Modelsmentioning

confidence: 99%

A Novel Physically Guided Data Fusion Prediction Model for Micro-EDM Drilling

Cheng,

Liu,

Cheng

et al. 2023

Materials

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“…The EDM process involves a wide range of phenomena, such as phase transitions [79][80][81], melt behavior [82][83][84], oxidation [36,50,85], residual stresses [86][87][88][89][90], energy transfer, absorption and dissipation [91][92][93][94], chemical reactions [75,95], microstructural changes [53,81,96], etc. The EDM process is generally based on the melting and evaporation of material under the influence of highly concentrated electrical energy, which is converted into thermal energy.…”

Section: Edm Process Physicsmentioning

confidence: 99%

“…In ceramic composites, depending on the strength of the discharge, it is possible to observe the breakage or fracture of reinforcing fibers and brittle fracture of the matrix [102]. The EDM process involves a wide range of phenomena, such as phase transitions [79][80][81], melt behavior [82][83][84], oxidation [36,50,85], residual stresses [86][87][88][89][90], energy transfer, absorption and dissipation [91][92][93][94], chemical reactions [75,95], microstructural changes [53,81,96], etc.…”

Section: Edm Process Physicsmentioning

confidence: 99%

“…The main technology used in modeling surface changes is the "killing" of elements when a certain material melt temperature is reached with the formation of a new surface type [54,133,135,191,201]. There are studies related to modeling the process of removing material by obtaining the actual energy of the discharge during a spark [93]. Ni et al [204] reported the research in which the material was removed according to the algorithm associated with the minimum gap leading to the discharge.…”

Section: Numerical Simulation Of the Edm Processmentioning

confidence: 99%

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Recent Trends and Developments in the Electrical Discharge Machining Industry: A Review

Kamenskikh,

Muratov,

Shlykov

et al. 2023

JMMP

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Electrical discharge machining (EDM) is a highly precise technology that not only facilitates the machining of components into desired shapes but also enables the alteration of the physical and chemical properties of workpieces. The complexity of the process is due to a number of regulating factors such as the material of the workpiece and tools, dielectric medium, and other process parameters. Based on the material type, electrode shape, and process configuration, various shapes and degrees of accuracy can be generated. The study of erosion is based on research into processing techniques, which are the primary tools for using EDM. Empirical knowledge with subsequent optimization of technological parameters is one of the ways to obtain the required surface quality of the workpiece with defect minimization, as well as mathematical and numerical modeling of the EDM process. This article critically examines all key aspects of EDM, reflecting both the early foundations of electrical erosion and the current state of the industry, noting the current trends towards the transition of EDM to the 5.0 industry zone in terms of safety and minimizing the impact of the process on the environment.

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