TY  - CONF
ID  - russ2008detection
T1  - Detection of Faulty Products using Data Mining
A1  - Karim, Md A.
A1  - Ruß, Georg
A1  - Islam, Aminul
TI  - Computer and Information Technology, 2008. ICCIT 2008. 11th International Conference on
Y1  - 2008
SP  - 101
EP  - 107
UR  - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4803116
M2  - doi: 10.1109/iccitechn.2008.4803116
KW  - Data Mining
KW  - fault diagnosis
KW  - faulty product detection
KW  - flaw detection
KW  - growing self-organizing map algorithm
KW  - large database
KW  - manufactured products
KW  - manufacturing data mining
KW  - manufacturing data processing
KW  - manufacturing process
KW  - manufacturing processes
KW  - pattern clustering
KW  - product development
KW  - product quality
KW  - product yield
KW  - quality control
KW  - self-organising feature maps
KW  - unsupervised clustering
KW  - very large databases
N2  - The manufacturing process is complex due to the large number of processes, diverse equipment set and nonlinear process flows. Manufacturers constantly face yield and quality problems as they constantly redesign their processes for the rapid introduction of new products and adoption of new process technologies. Solving product yield and quality problems in a manufacturing process is becoming increasingly difficult. There are various types of failures and their causes have complex multi-factor interrelationships. High innovation speed forced today's manufacturers to find failure causes quickly by examining the historical manufacturing data. Data mining offers tools for quick discovery of relationships, patterns, and knowledge in large databases. This has been applied to many fields such as biological technology, financial analysis, medical information, etc. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self-organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some manufacturing data. Moreover, there was no benchmark to monitor improvement in clustering. In this study a method has been proposed to evaluate quality of the clusters produced by GSOM and to remove insignificant variables from the dataset. With the proposed modifications, significant improvement in unsupervised clustering was achieved with complex manufacturing data. Results show that the proposed method is able to effectively differentiate good and faulty products.
ER  -