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Integration of cuckoo search and fuzzy support vector machine for intelligent diagnosis of production process quality

  • * Corresponding author: Xudong Chen

    * Corresponding author: Xudong Chen

The work is supported by by the [Soft Science Project for Science & Technology Department of Sichuan Province #1] under Grant [number 20RKX0474]; [Soft Science Project for Chengdu Science and Technology Agency #2] under Grant [number 2020-RK00-00179-ZF]; [Sichuan Civil-military Integration Research Center #3] under Grant [number JMRH-1807]

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  • The quality of High-tech products usually influenced by numerous cross-correlation quality characteristics in production process. However, traditional quality control method is difficult to satisfy the requirement of monitoring and diagnosing multiple related quality characteristics. Scholars found that the diagnosis effect of support vector machine method is better than others. But, constructing fuzzy support vector machine for diagnosis by calculating the sample membership degree from the sample point to the class center is vulnerable to the influence of sample noise points because it will lead to low accuracy rate. Therefore, this paper focus on exploring the issue about the abnormal pattern and intelligent diagnosis of interrelated multivariable process quality, by taking the multivariable quality characteristics of capacitor as research object. Using multivariate exponentially weighted moving average (MEWMA) control chart to joint monitor the multiple quality characteristics. Constructing a fuzzy support vector machine (FSVM) based on cloud calculative model and cuckoo search (CS) for intelligent diagnosis on abnormal pattern. The result showed that the diagnostic accuracy rate for sample data is 97.42%. In instance analysis, the average diagnosis accuracy rate is 95.60%. It verifies the CS-FSVM model has a good diagnosis performance.

    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

    Citation:

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  • Figure 1.  The relevant literatures of multivariate quality control

    Figure 2.  Cloud model of sample "standard" and its digital characteristics

    Figure 3.  The flow diagram of cuckoo research algorithm

    Figure 4.  Fractal visualization diagram of sample data and category labels

    Figure 5.  Fractal visualization diagram of sample data and category labels

    Figure 6.  Fitness curve of original sample parameter optimization by cuckoo search algorithm

    Figure 7.  Fitness curve of optimization of sample parameters with degree of membership by cuckoo search algorithm

    Figure 8.  Classification results of 7 types of abnormal test for mean deviation of variables $ D $, $ E $ and $ L $

    Figure 9.  The degree of membership of quality characteristics of $ D $ and $ E $

    Figure 10.  Classification results of 7 types of abnormal test of mean deviation for variables $ D $, $ E $ and $ L $ with degree of membership

    Figure 11.  Comparison of actual classification and prediction classification of original sample test set

    Figure 12.  Comparison of actual classification and prediction classification of sample test sets with degree of membership

    Figure 13.  MEWMA control chart for chip diameter (d) and height (h)

    Figure 14.  The optimization results for parameters $ C $ and $ g $

    Figure 15.  Comparison between actual classification and prediction classification of sample test set

    Table 1.  The abnormal pattern of $ P = 3 $

    Number of abnormal variables Production process status Combination patterns Output value
    No abnormity (0, 0, 0)
    One Mean-shift of first variable (1, 0, 0) 1
    Mean-shift of Second variable (0, 1, 0) 2
    Mean-shift of Third variable (0, 0, 1) 3
    Two Mean-shift of first and second variable (1, 1, 0) 4
    Mean-shift of first and third variable (1, 0, 1) 5
    Mean-shift of second and third variable (0, 1, 1) 6
    Three Mean-shift of three variables (1, 1, 1) 7
     | Show Table
    DownLoad: CSV

    Table 2.  Comparison of abnormity diagnostic effect

    Type Mean offset Diagnostic accuracy
    CS-FSVM CS-SVM FSVM SVM
    (1, 0, 0) $ (3\sigma, 0, 0) $ 96.74% 86.44% 88.28% 78.86%
    (0, 1, 0) $ (0, 3\sigma, 0) $ 98.15% 88.5% 90.36% 86.34%
    (0, 0, 1) $ (0, 0, 3\sigma) $ 97.24% 89.33% 92.54% 88.52%
    (1, 1, 0) $ (3\sigma, 3\sigma, 0) $ 96.65% 90.65% 89.25% 84.3%
    (1, 0, 1) $ (3\sigma, 0, 3\sigma) $ 98.5% 87.5% 89.37% 82.65%
    (0, 1, 1) $ (0, 3\sigma, 3\sigma) $ 97.26% 88.26% 88.42% 79.64%
    (1, 1, 1) $ (3\sigma, 3\sigma, 3\sigma) $ 97.71% 80.71% 82.77% 80.22%
    Average diagnostic accuracy 97.42% 87.34% 88.71% 82.93%
     | Show Table
    DownLoad: CSV

    Table 3.  Diagnosis results of application example for model

    Abnormal variable Combination pattern Diagnosis accuracy Average accuracy
    (d) (1, 0) 95.42% 95.60%
    (h) (0, 1) 96.08%
    (d) & (h) (1, 1) 95.31%
     | Show Table
    DownLoad: CSV
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