Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Chen, Yanyan; Hu, Shunchang; Li, Ansheng; Cao, Yang; Zhao, Yue; Ming, Wuyi

doi:10.3390/met13050839

Cited by 18 publications

(11 citation statements)

References 123 publications

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“…Single-spark FEM models excel in predicting workpiece surface crater profiles and can be extended to multi-spark scenarios to anticipate the surface structures achieved in EDM [32]. The EDM process faces several challenges, including its low machining efficiency, high SR, and difficulties in meeting high-precision requirements, which constrain its broad applicability [33]. Therefore, this section offers a comprehensive review of FEM models for predicting four critical performance indicators when evaluating EDM's performance: the MRR, SR, TWR, and RLT.…”

Section: Single-spark Simulationsmentioning

confidence: 99%

Progress in Simulation Modeling Based on the Finite Element Method for Electrical Discharge Machining

Li,

Sun,

Xing

et al. 2023

Metals

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Electrical Discharge Machining (EDM) is a machining method commonly used to produce complex shapes and deep holes by eroding hard metals with an electric arc. There is a growing demand for process simulation using finite element models in order to improve the quality and efficiency of EDM, to reduce costs, to improve resource efficiency, and to facilitate its application in critical areas such as aerospace and mechanical engineering. Finite element models have greatly improved the prediction accuracy of EDM processes, simulated complex hybrid machining processes, and provided important guidance for the optimization of EDM processes. This paper systematically reviews the research progress of finite element modeling for EDM. Finite element method modeling is evaluated mainly in terms of four indicators: material removal rate, surface roughness, tool wear ratio, and recast layer thickness. Firstly, the importance and application of EDM are described, and the EDM finite element method modeling and its advantages are summarized. Then, the single-spark simulation model and the multi-spark simulation model of EDM are compared and discussed. Among the mainstream finite element models, the prediction error of the material removal rate for single-spark simulation ranges from 8.2% to 14.75%, while the prediction error of the recast layer thickness for multi-spark simulation can be as low as 1.98%. Finally, the applications of finite element modeling in EDM hybrid machining processes’ performance prediction and new material machining are summarized, and future research directions and trends in EDM finite element modeling are predicted.

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Section: Single-spark Simulationsmentioning

confidence: 99%

Progress in Simulation Modeling Based on the Finite Element Method for Electrical Discharge Machining

Li,

Sun,

Xing

et al. 2023

Metals

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“…Electric discharge machining (EDM) process enhancement heavily relies on computational programming. This method entails creating a mathematical representation that illustrates the connections between different parameters related to machining and the intended process results, including tool wear, surface finish, along material removal rate (MRR) [8]. The best settings can be found by methodically experimenting with various combinations of machining parameters thanks to algorithms such as integer, nonlinear, and linear programming.…”

Section: B Mathematical Programming In Edm Process Optimizationmentioning

confidence: 99%

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Ritu Gupta

2023

IJRITCC

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This study explores the combination of machine guidance and several developing approaches to enhance both precision and effectiveness during Electricity-discharged Machining (EDM) business operations. The studies on quality control, energy efficiency, sustainable development, mathematical modelling within EDM optimization, and machine learning applications in EDM optimisation are all examined in this study. It highlights significant gaps in scientific knowledge, providing a pathway for the development of state-of-the-art EDM methods. The outcomes show that material decrease, energy efficiency, along EDM technique optimisation can all be enhanced. This study offers valuable information for future research within the field and contributes to the ongoing conversation about advanced manufacturing techniques. This project intends to revolutionise EDM by merging mathematical programming and machine learning. Three primary topics are investigated machining parameter optimisation, efficiency improvement using machine learning and environmental effect assessment. The goals of the study are met by using the deductive method, which gives a formal setting in which to examine hypotheses. Descriptive research designs allow for in-depth analyses of previously published works, mathematical models and automated learning programs. Finding commonalities and trends in qualitative data is the goal of the thematic data analysis technique. The results of this study provide useful resources, standards and sustainable perspectives for enhancing EDM procedures in manufacturing settings.

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“…A significant development in micro-EDM involves the use of coated electrodes, a progressive technical solution designed to enhance both technical capabilities and economic efficiency [6,7]. This innovation has garnered attention for its potential to revolutionize micro-EDM processes [8].…”

Section: Introductionmentioning

confidence: 99%

Investigating Technological Parameters and TiN-Coated Electrodes for Enhanced Efficiency in Ti-6Al-4V Micro-EDM Machining

Pham,

Nguyen,

Shailesh

et al. 2024

Metals

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Micro-electrical discharge machining (micro-EDM) stands out as a transformative methodology, offering substantial progress in both technical and economic efficiency through the integration of coated electrodes. This study meticulously analyzes various technological parameters in micro-EDM, focusing specifically on Ti-6Al-4V, a widely employed titanium alloy. The application of a titanium nitride (TiN) coating material on a tungsten carbide (WC) electrode is investigated using the Taguchi method of experimental design. This study employs an ANOVA and factorial design methodology to scrutinize the influence of key parameters, namely voltage (V), capacitance (C), and spindle rotation (in revolutions per minute) (RPM) on the tool wear rate (TWR), overcut (OVC), and Z coordinate (depth) within the micro-EDM process. The findings unveil a noteworthy increase in the TWR with an elevated V, C, and RPM, with capacitance exerting a pronounced influence while voltage exhibits the least impact. OVC exhibits notable variations, revealing an inverse relationship with RPM. The Z coordinate (depth) is significantly affected by capacitance, with voltage and RPM each having a relatively negligible impact. A surface quality analysis exposes similarities and numerous defects in both coated and uncoated electrodes, emphasizing the need for further exploration into the effectiveness of coated electrodes in enhancing post-micro-EDM machined surface layers. This study contributes valuable insights to optimize and advance micro-EDM processes, laying groundwork for future innovations in precision machining.

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Parameters Optimization of Electrical Discharge Machining Process Using Swarm Intelligence: A Review

Cited by 18 publications

References 123 publications

Progress in Simulation Modeling Based on the Finite Element Method for Electrical Discharge Machining

Progress in Simulation Modeling Based on the Finite Element Method for Electrical Discharge Machining

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

Investigating Technological Parameters and TiN-Coated Electrodes for Enhanced Efficiency in Ti-6Al-4V Micro-EDM Machining

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