Prediction and remediation of failed product identification based on manufacturing history data

Jian Wang, Weiping He, Xiashuang Li, Gaifang Guo

Research output: Contribution to journalArticlepeer-review

Abstract

Aiming at the problem that product identification was not read due to wear, pollution and other factors caused by production environment and process complexity of discrete manufacturing enterprise, a prediction and remediation method of failed product identification was presented based on manufacturing history data. The product manufacturing history data model based on Direct Part Marking (DPM) was given. The factors of failed product identification were analyzed and the history data was standardized by Z-score, and the extracted feature was optimized through Principle Component Analysis (PCA) method. The neural network model for prediction failed product identification was established, and product identification was remedied by using neural network prediction results with identification transfer and inheritance method. The experimental results showed that the proposed method could better predict or remedy the failed product identification.

Original languageEnglish
Pages (from-to)2494-2503
Number of pages10
JournalJisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
Volume21
Issue number9
DOIs
StatePublished - 1 Sep 2015

Keywords

  • Direct part marking
  • Manufacturing history data
  • Neural networks
  • Prediction and remediation
  • Principal component analysis

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