journal article·2024-03-29Open access

Study on Improving Electrochemical Machining Performances through Energy Conversion of Electrolyte Fluid

Zhenghui Ge, Maolong Chen, Wangwang Chen, Yongwei Zhu

Coatings· Volume 14· Issue 4

Abstract

Electrochemical machining (ECM) is regarded as a promising and cost-effective manufacturing method for difficult-to-cut materials with complex shapes and structures. The flow-field state of machining gaps is considered a key factor affecting machining performance in ECM engineering practice and has been widely studied. However, little attention has been given to the fluid energy of electrolytes during the ECM process. This study mainly focuses on the influence of the conversion between dynamic and static pressure energy of electrolyte fluid on ECM performance. The simulation results show that by changing the degree of convergence of the electrolyte outlet, the dynamic and static pressure energy of the electrolyte can be effectively adjusted, and increased static pressure energy can be obtained by sacrificing dynamic pressure energy. The experimental results show that electrolyte energy conversion can achieve better surface quality and material removal rate (MRR). However, excessive sacrifice of fluid dynamic pressure energy will also worsen the ECM performance. By combining MRR and Ra, moderate fluid energy conversion can achieve better machining performance, with a degree of convergence of around 50%–70%. The experimental results also show that moderate energy conversion of the electrolyte fluid can improve the utilization efficiency of electrical energy in the ECM process. This may be because the static pressure of the electrolyte can effectively compress the volume of gas products and reduce the electrical resistivity of the machining gap. These conclusions can provide some useful assistance for ECM engineering practice.
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Research context

OpenAlex topics: Advanced Machining and Optimization Techniques · Advanced machining processes and optimization · Advanced Surface Polishing Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China

Funding

Jiangsu Agricultural Science and Technology Innovation Fund · National Natural Science Foundation of China

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