Implementation and Application of Logistic-Regression-Based MSQIM : Quality Data Mining through the Multi-Stage Quality Information Model (MSQIM) (1st Report)

DOI Web Site 1 Citations 13 References

Bibliographic Information

Other Title
  • ロジスティック回帰援用型多段階品質情報推移モデルの提案 : 多段階品質情報推移モデルに基づく品質データマイニングの研究(第1報)
  • ロジスティック カイキ エンヨウガタ タダンカイ ヒンシツ ジョウホウ スイイ モデル ノ テイアン タダンカイ ヒンシツ ジョウホウ スイイ モデル ニ モトヅク ヒンシツ データマイニング ノ ケンキュウ ダイ1ポウ

Search this article

Abstract

Detecting causal factors of chronic quality defects is very important, and often the most critical step of improving manufacturing quality in a multi-stage production system. Since advanced information technology has enriched manufacturing databases, it is now the time to ask how to utilize databases to streamline the process of this causal factor detection. However, applying conventional multivariate statistical analysis methods or modern data mining approaches simply to a database does not always provide sufficient knowledge for revealing the key factors of chronic defects and how they cause the defects. Thus, the authors propose a novel framework for more sophisticated exploratory quality data analysis in order to support detection of the causal factors. The proposed data analysis framework is named the "multi-stage quality information model (MSQIM)", which, if possible, should establish some hypotheses on the causal factors and/or the defect-causing mechanisms, and should at least identify which process steps within the production system further efforts of causal factor detection should be focused on. MSQIM first divides a manufacturing database into several segments, each of which corresponds to a certain process step within the production system. It then traces how the amount of information on the resultant manufacturing quality varies along with the process steps so as to identify the relevant process steps that require further focus. The varying pattern of the quality information is also studied in a qualitative way so that it assists in hypothesis generation. This paper mainly discusses how to implement MSQIM based on logistic regression. It also demonstrates how the proposed approach works through an industrial example.

Journal

Citations (1)*help

See more

References(13)*help

See more

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top