Data analytics to reduce stop-on-fail test in electronics manufacturing

Open Computer Science

Walshaw Chris¹  Herrera Ana Elsa Hinojosa¹  Yin Chunyan¹  Bailey Chris¹  Stoyanov Stoyan¹ 

[1] School of Computing and Mathematical Sciences, University of Greenwich. LondonSE10 9LS. United Kingdom.;

关键词: decision tree;    logistic regression;    random forest;    incomplete dataset;    electronic device;   

DOI  :  10.1515/comp-2019-0014

来源: DOAJ

【 摘 要 】

The use of data driven techniques is popular in smart manufacturing. Machine learning, statistics or a combination of both have been used to improve processes in electronic manufacturing. This paper presents the application of classical techniques to reduce production cycle time by compacting a production test sequence. This set of tests is run on stop-on-fail scenario for quality assurance of an electronical device. Data generated in the production test-set on stop-on-fail scenario challenges the traditional application of the data driven techniques, because of the missing data characteristic. The developed computational procedures handle this application-specific data attribute. The novelty of this work is in the algorithm developed, which applies classical techniques in an iterative environment, as a strategy to analyse incomplete datasets. Results show that the method can reduce a production test set with parametric and non-parametric tests by building an accurate prognostic model. The results can reduce production cycle time and costs. The paper details and provides discussions on the advantages and limitations of the proposed algorithms.

【 授权许可】

Unknown