Modeling of Surface Roughness and the Role of Debris in Micro-EDM

2006 ◽  
Vol 129 (2) ◽  
pp. 265-273 ◽  
Author(s):  
M. P. S. Krishna Kiran ◽  
Suhas S. Joshi

Surface roughness is one of the important quality characteristic of a micromachined component. This paper presents a model to predict surface roughness of micro-EDmachined surfaces. The model is based on the configuration of a single-spark cavity formed as a function of process parameters. Assuming the normal distribution of surface heights, the μ and σ(Rq) of the surface profile are evaluated after every spark. The model was further extended to capture the role of debris in micro-EDM in changing electric potential at the micropeaks on the cathode surfaces. The chemical kinetics approach was used to evaluate the change in plasma enthalpy and composition as a result of debris inclusion in the dielectric. The corresponding energy distribution between the electrodes was used to predict configuration of the single-spark cavity and the consequent surface roughness using the earlier surface roughness model. The modeling results were found to agree well with the micro-EDM validation experiments performed without and with the inclusion of artificial debris (iron particles) in the dielectric.

2021 ◽  
Vol 15 (1) ◽  
pp. 17-23
Author(s):  
Ming Feng ◽  
Youliang Wang ◽  
Yongbo Wu ◽  
◽  
◽  
...  

Zirconia ceramics have excellent applicability in the aerospace, defense, new energy, automotive, electronics, and biomedical fields. However, few investigations have been conducted on the high-precision polishing of zirconia ceramics. In this work, a polishing method using a magnetic compound fluid slurry is proposed. First, the principle and the constructed experimental setup were presented. Then, the experiments were performed that characterized the surface profile after polishing, the effect of the working gap, and the effect of the concentration of carbonyl iron particles (CIPs) on the material removal and surface quality. The results showed that the material removal ability correlated positively with the surface roughness; the smallest working gap (0.5 mm) induced greater material removal ability and better surface roughness; higher CIP concentration enabled a higher polishing force to obtain higher material removal and better surface quality. The polishing results show that surface roughness Rz of 55 nm was obtained at the surfaces of zirconia ceramics, confirming that the proposed method has the potential for polishing of zirconia ceramics.


2020 ◽  
Vol 527 ◽  
pp. 146799 ◽  
Author(s):  
Zhanglei Zhu ◽  
Wanzhong Yin ◽  
Donghui Wang ◽  
Haoran Sun ◽  
Keqiang Chen ◽  
...  
Keyword(s):  

2017 ◽  
Vol 61 (2) ◽  
pp. 295-303 ◽  
Author(s):  
Nihat A. Isitman ◽  
András Kriston ◽  
Tibor Fülöp

2014 ◽  
Vol 989-994 ◽  
pp. 3331-3334
Author(s):  
Tao Zhang ◽  
Guo He Li ◽  
L. Han

High speed milling is a newly developed advanced manufacturing technology. Surface integrity is an important object of machined parts. Surface roughness is mostly used to evaluate to the surface integrity. A theoretical surface roughness model for high face milling was established. The influence of cutting parameters on the surface roughness is analyzed. The surface roughness decreases when the cutter radius increases, total number of tooth and rotation angular speed, while it increases with the feeding velocity. The high speed face milling can get a smooth surface and it can replace the grinding with higher efficiency.


2010 ◽  
Vol 102-104 ◽  
pp. 610-614 ◽  
Author(s):  
Jun Chi ◽  
Lian Qing Chen

A methodology based on relax-type wavelet network was proposed for predicting surface roughness. After the influencing factors of roughness model were analyzed and the modified wavelet pack algorithm for signal filtering was discussed, the structure of artificial network for prediction was developed. The real-time forecast on line was achieved by the nonlinear mapping and learning mechanism in Elman algorithm based on the vibration acceleration and cutting parameters. The weights in network were optimized using genetic algorithm before back-propagation algorithm to reduce learning time.The validation of this methodology is carried out for turning aluminum and steel in the experiments and its prediction error is measured less than 3%.


Langmuir ◽  
2003 ◽  
Vol 19 (8) ◽  
pp. 3304-3312 ◽  
Author(s):  
Javier Sanchez-Reyes ◽  
Lynden A. Archer

2006 ◽  
Vol 18 (17) ◽  
pp. 4143-4160 ◽  
Author(s):  
U Tartaglino ◽  
V N Samoilov ◽  
B N J Persson
Keyword(s):  

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