Title page for etd-0419118-080930


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URN etd-0419118-080930
Author Yung-Cheng Hsu
Author's Email Address n044020008@student.nsysu.edu.tw
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Department Information Management
Year 2017
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Using Decision Tree Algorithm for the Detection and Decision Rule Construction of Defect on Dispensing Process
Date of Defense 2018-04-16
Page Count 63
Keyword
  • C5.0
  • Decision tree
  • Random forest
  • Data mining
  • Logistic regression
  • Abstract In recent years, due to the advancement of information technology, the analysis and research of big data has been widely applied in various industrial fields. The new thinking that brings different industries to various industries lies in the application of industrial innovation. Surface Mount Technology (SMT) - Dispensing Processes often used in IC packaging and testing, can easily lead to the occurrence of defective products due to many accidental factors, defects are then discovered through product inspection and testing after the dispensing process.
      In addition to the low-yield caused by the production, the cost of the production materials, and the time and labor spent for follow-up analysis, are quite expensive for the manufacturing industry.
      This study intends to establish a predictive model through data mining technique and identy decision rules. As a reference for defect analysis, the set of decision rules can be used for adjusting operational inspections, including judging whether the operating status of the inspection results is appropriate for production, and making decisions to avoid/reduce the occurrence of defects opportunely.
      In this study, we try to identify some data fields that are highly related to defect type as verified by Logistic regression. The classification model was established using random forest and decision tree C5.0, and potential rules were discovered. The results show the predict model achieves the average prediction accuracy 98.5%, the average sensitivity 76.33%, and the average specificity 99.86%. The extracted rules are consistent with the conventional wisdom known to the experts.
    Advisory Committee
  • Shih-Chieh Hsu - chair
  • Hui-Mei Hsu - co-chair
  • San-Yih Hwang - advisor
  • Files
  • etd-0419118-080930.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2018-05-29

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