Hargovind Soni
National Institute of Technology Delhi, University of Johannesburg
Papers
2
Total Citations
8
H-Index
2
About
Hargovind Soni is a researcher advancing the frontiers of smart manufacturing and advanced materials processing. His work centers on the optimization of non-traditional machining processes, particularly wire electric discharge machining (WEDM), for difficult-to-machine materials. Soni’s key contributions lie in integrating artificial intelligence and multi-criteria decision-making tools to enhance manufacturing precision and efficiency. Notably, his 2023 study on "Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing" (6 citations) demonstrates how AI can optimize WEDM processes for Industry 4.0 environments, bridging the gap between traditional machining and cyber-physical production systems. His earlier work, "Enhanced process parameters using TOPSIS method during wire electro discharge machining of TiNiCo shape memory alloy" (2 citations), addresses the critical challenge of machining shape memory alloys—materials prized in biomedical, aerospace, and robotics applications for their superelasticity and shape memory effects. By applying the TOPSIS method, Soni established optimal parameters for machining these complex alloys, enabling their broader industrial use. His research is foundational for students and engineers seeking to implement data-driven, sustainable manufacturing solutions in high-tech sectors.
Research Focus
Key Achievements
Top Papers
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- 2