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Superalloys have become integral to advanced engineering sectors due to their exceptional thermal stability, corrosion resistance, and strength retention at high temperatures. These superior properties, however, present significant challenges to conventional machining methods, making electric discharge machining (EDM) a critical non-traditional process for the precise shaping of nickel-, titanium-, and cobalt-based superalloys. This review synthesizes research developments in the EDM of these materials, focusing on their electro-thermal behavior, process parameters, and microstructural responses. A thorough examination of existing literature indicates a predominant focus on nickel-based alloys, especially the Inconel series, while alloys such as René 80, Waspaloy, Udimet 720, and Stellite, L-605 have been less extensively studied. The review explores how EDM process input variables such as discharge energy, pulse timing, dielectric composition, and electrode characteristics collectively affect material subtraction rate, surface morphology, and metallurgical transformations. Innovations in powder-mixed, ultrasonic-assisted, cryogenic, and near-dry-EDM configurations are assessed for their potential to improve machining efficiency, reduce surface degradation, and support sustainable processing. Recent progress in biodegradable dielectrics, energy-efficient control systems, and hybrid EDM architectures suggests a shift towards eco-friendly and digitally enhanced manufacturing. Additionally, the integration of AI-based modeling, digital twins, and data-driven optimization frameworks heralds the emergence of intelligent EDM systems capable of real-time adaptation and predictive control. By consolidating experimental insights, material-specific trends, and emerging technological directions, this review offers a comprehensive understanding of EDM’s current state and its transformative role in the sustainable and intelligent machining of advanced superalloys.

BharatSim is an open-source agent-based modelling framework for the Indian population. It can simulate populations at multiple scales, from small communities to states. BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. This synthetic population defines individual agents with multiple attributes, among them age, gender, home and work locations, pre-existing health conditions, and socio-economic and employment status. BharatSim’s domain-specific language provides a framework for the simulation of diverse models. Its computational core, coded in Scala , supports simulations of a large number of individual agents, up to 50 million. Here, we describe the design and implementation of BharatSim, using it to address three questions motivated by the COVID-19 pandemic in India: (i) When can schools be safely reopened given specified levels of hybrid immunity?, (ii) How do new variants alter disease dynamics in the background of prior infections and vaccinations? and (iii) How can the effects of varied non-pharmaceutical interventions (NPIs) be quantified for a model Indian city? Through its India-specific synthetic population, BharatSim allows disease modellers to address questions unique to this country. It should also find use in the computational social sciences, potentially providing new insights into emergent patterns in social behaviour.