Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1838-1843.doi: 10.13229/j.cnki.jdxbgxb20170798

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Evaluation of surface roughness of micro electrical discharge machining based-on fast Gauss algorithm

ZHAO Shuang1(),SHEN Ji-hong2,ZHANG Liu3,ZHAO Han4,CHEN Ke-fan4   

  1. 1. College of Automation, Harbin Engineering University, Harbin 150001,China
    2. College of Science, Harbin Engineering University, Harbin 150001, China
    3. College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130061, China;
    4. College of Aerospace and Civil Engineering, Harbin Engineering University, Harbin 150001,China
  • Received:2017-08-01 Online:2018-11-20 Published:2018-12-11

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Abstract:

In order to realize micro Electrical Discharge Mchining (EDM) surface roughness evaluation, a fast Gauss evaluation algorithm of surface roughness is proposed based on the results of scanning electron microscopy (SEM). First, the Gauss evaluation algorithm is described in detail, and the data to be evaluated are preprocessed. Then, according to the SEM data of the micro electrical discharge machining, a fast evaluation algorithm is designed based on a Gauss Evaluation benchmark. Finally, with the input of the results obtained from micro electrical discharge machining and SEM on actual samples, the simulation experiments of surface roughness evaluation are carried out using the proposed fast Gauss evaluation method, convolution algorithm and FFT algorithm respectively. Experimental results show that, compared with the other algorithms, the proposed fast Gauss evaluation method significantly improves the efficiency of evaluation. Estimated time for 512×512 sampling points can be shortened to 2 s from 6 s and 4s of the other two conventional methods respectively.

Key words: automation control technology, micro electrical discharge machining (micro-EDM), surface roughness, fast Gauss method, evaluation of surface roughness

CLC Number: 

  • TP274

Fig.1

Flow chart of the algorithm"

Fig.2

Moving path of the probe"

Fig.3

D3100 scanning image"

Fig.4

Original figure of the experimental workpiece"

Fig.5

Sample data of the workpiece surface"

Fig.6

Basic surface of the workpiece after rapid Gauss filter"

Fig.7

Comparison of evaluating time of three algorithms"

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