Traditional and non-traditional machining technology of metallic glass

Du, Jinguang; Tian, Bo; Duan, Liuyang; Ming, Wuyi; Liu, Kun; He, Wenbin

doi:10.1007/s00170-022-08923-9

Cited by 10 publications

(7 citation statements)

References 99 publications

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“…Most of the existing literature on micro-EDM remains at the laboratory stage, mainly focusing on the research of processing mechanisms. Indeed, micro-ED is a branch of EDM, and the machining mechanism of electrical discharge machining, like other special machining, is very complex [112][113][114]. The existing research has not fully understood its working mechanism [3,[115][116][117].…”

Section: Discussionmentioning

confidence: 99%

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

Chen

et al. 2023

Metals

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Electrical discharge machining (EDM) can use soft tool electrodes to process hard workpieces to achieve “soft against hard”, because it directly uses electrical energy and thermal energy to remove metal materials. Then, it can generate complex features on harder materials and meet the requirements of excellent surface quality. Since EDM involves many process parameters, including electrical parameters, non-electrical parameters, and materials properties, it is essential to optimize its process parameters to obtain good performance. In this direction, the application of the swarm intelligence (SI) technique has become popular. In this paper, the existing literature is comprehensively reviewed, and the application of the SI technique in the optimization of EDM process parameters is summarized. Sinker-EDM (SEDM), wire-EDM (WEDM), and micro-EDM (MEDM) with various hybrid techniques are among the EDM methods considered in this study because of their broad adoption in industrial sections. The fundamental nature of all review articles will assist engineers/workers in determining the process parameters and processing performance, the SI algorithm, and the optimal technique by which to obtain the desired process parameters. In addition, discussions from the perspectives of the similarity, individuality, and complementarity of various SI algorithms are proposed, and necessary outlooks are predicted, which provides references for the high performance of the EDM processes in the future.

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Section: Discussionmentioning

confidence: 99%

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

Chen

et al. 2023

Metals

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“…Machining techniques for metallic glass components vary depending on size (i.e., micrometer to macrometer scales). [22,228] Typical cutting processes to reduce dimensions in macroscopic specimens have specific challenges associated with the low thermal conductivity of most metallic glasses, which can result in very significant and rapid heating of the part, to the point that surface crystallization and oxidation (if cutting is in air) may occur. As an example, significant thermal radiation was seen during lathe turning of a Zr 41.2 Ti 13.8 Cu 12.5 Ni 10.0 Be 22.5 amorphous alloy, [229] a sign of the higher temperatures of the sample resulting in surface oxidation.…”

Section: Machining and Cutting Techniquesmentioning

confidence: 99%

Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses

et al. 2023

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In this review, two interrelated areas are focused on for the development of novel amorphous metallic alloys, namely, materials processing and machine learning techniques for the design of new alloy compositions. Findings, barriers, and opportunities are described, targeting powder production and sintering, additive manufacturing, and postprocessing techniques, followed by the latest developments in artificial intelligence algorithms for both the design of new alloys and for alloy classification tasks.

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“…Tool wear is a prevalent and unavoidable phenomenon in EDM, with significant implications for machining precision, surface quality, and efficiency [110,111]. The monitoring and analysis of tool wear facilitate timely interventions to preserve machining quality and prolong the tool's lifespan.…”

Section: Tool Wear Ratiomentioning

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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Traditional and non-traditional machining technology of metallic glass

Cited by 10 publications

References 99 publications

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

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

Latest Advances in Manufacturing and Machine Learning of Bulk Metallic Glasses

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

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