An empirical model for controlling characteristics of micro channel machined using abrasive assisted electrochemical jet machining

被引:1
作者
Liu, Zhuang [1 ]
Gao, Changshui [1 ]
Zhao, Kai [1 ]
Guo, Chao [2 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, 29 Yudao St, Nanjing 210016, Jiangsu, Peoples R China
[2] Jiangsu Key Lab Precis & Micromfg Technol, 29 Yudao St, Nanjing 210016, Jiangsu, Peoples R China
来源
19TH CIRP CONFERENCE ON ELECTRO PHYSICAL AND CHEMICAL MACHINING | 2018年 / 68卷
基金
中国国家自然科学基金;
关键词
abrasive assisted electrochemical jet machining; electrochemical jet machining; micro machining; micro channel; channel characteristics; BIPOLAR PLATES; CORROSION; EROSION; MICROCHANNEL; FABRICATION; ALUMINUM; BEHAVIOR; MARINE;
D O I
10.1016/j.procir.2017.12.087
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Metallic micro channels provide functions of fluid feed and heat exchange in fields of fuel cell, MEMS component, micro mold and heat exchanger. Abrasive assisted electrochemical jet machining (AECJM) is a hybrid manufacturing technology coupling erosion and corrosion concurrently to remove metals. The AECJM shows a great potential in machining micro channels at metallic surface. From production point of view, it is highly necessary to control the channel characteristics, e.g. width and depth, during the AECJM operation to meet possible specifications from industries. This paper presents an empirical model to predict width and depth of micro channels machined in stainless steel 304 using AECJM with electrolytic solution of NaNO3, abrasives of Al2O3 and DC working voltage. The empirical model was established on a basis of non-linear regression analysis with process variables of working voltage, abrasive concentration, solution concentration, jet pressure, jet scan speed and jet scan passes. Verified results show that the empirical model can predict channel width and depth with a deviation from 6.3% to 4.4% for most cases before process operation. However, the strategies of determining machining parameters are highly important when applying the empirical model to reach a specific machining target. (C) 2018 The Authors. Published by Elsevier B.V.
引用
收藏
页码:724 / 729
页数:6
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