Rapid fault cause identification in surface mount technology processes based on factory-wide data analysis
Author(s) -
Dongil Kim,
Jeongin Koo,
HyeIn Kim,
Seokho Kang,
Sang Hyun Lee,
Jeong Tae Kang
Publication year - 2019
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1177/1550147719832802
Subject(s) - computer science , visualization , data mining , factory (object oriented programming) , raw data , identification (biology) , partition (number theory) , fault (geology) , mount , row , histogram , real time computing , data set , reliability engineering , artificial intelligence , database , botany , seismology , engineering , biology , programming language , geology , operating system , mathematics , combinatorics , image (mathematics)
Surface mount technology is an important process in modern electronic circuit manufacturing. Quality control problems have arisen in this area because of the increased design and processing complexity of electronic circuits. Identifying the cause of a fault shortly after its occurrence is critical; however, human fault analysis is inaccurate and time-consuming. Here, we propose a data analysis method that provides actionable information that can easily be interpreted to facilitate rapid identification of fault cause in surface mount technology. The proposed method divides each input variable into a certain number of partitions, and then, the proportion of faults in a partition is calculated in comparison to the proportion of faults in the entire data set. The analytical results are provided to the user with a list that includes the fault causes and a corresponding density histogram for visualization. Real-world surface mount technology data were employed for a case study, in which raw data were preprocess...
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