TY  - EJOU
AU  - Paswan, Kamlesh 
AU  - Singh, Rajnish 
AU  - Singh, Vivekanand 
AU  - Singh, Brihaspati 
AU  - Saxena, Ankur 
AU  - Yadav, Chandrmani 

TI  - Physics-Based Modelling of Plasma-Material Interactions and Phase Transformations in Electrical Discharge Machining: A Computational Materials Perspective
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Electrical Discharge Machining (EDM) is governed by highly coupled, nonlinear electro-thermal-mechanical phenomena involving plasma-mediated energy transfer, rapid heat conduction, phase transformation, and resolidification over micro to nanosecond time scales. From a computational materials science perspective, EDM serves as a prototypical problem of extreme, localised energy–matter interaction, where predictive modelling requires rigorous treatment of multiphysics coupling and scale bridging. This review presents a critical synthesis of theoretical and numerical frameworks for modelling advanced EDM configurations, including vibration-assisted and turning-based EDM, powder- and nano-additive-assisted EDM, and alternative dielectric environments. The review consolidates continuum-based formulations that describe the evolution of the electric field, plasma channel dynamics, and transient heat transfer, typically governed by Maxwell’s equations coupled with Fourier and non-Fourier heat conduction models. Thermo-fluid and thermo-mechanical models accounting for melt flow, recoil pressure, surface tension, and thermal stress evolution are analysed for their ability to predict crater geometry, recast layer formation, and subsurface damage. The influence of externally imposed perturbations such as mechanical vibration, relative rotational kinematics, and particle-mediated plasma modulation is discussed through modifications in boundary conditions, energy partition coefficients, and effective transport properties. Multiscale modelling strategies that bridge discharge-scale plasma physics with mesoscale thermal fields and microscale material response are critically reviewed, including hybrid finite element–finite volume schemes and reduced-order models. In parallel, data-driven surrogate models and machine learning approaches are examined for parameter inference, uncertainty reduction, and rapid prediction of material behaviour. Major challenges related to model closure, scale separation, and experimental validation are identified, and future research directions are outlined toward fully coupled multiscale and digital twin frameworks for predictive EDM-induced material response.
KW  - Electrical discharge machining; plasma–material interaction; multiphysics and multiscale modelling; phase transformation; thermo-mechanical effects; machine learning-assisted EDM

DO  - 10.32604/cmc.2026.080581