Modeling complex manufacturing process activities using data mining: A rough set approach
Abstract
Printed circuit boards (PCBs) are essential part of modern electronic circuits. The manufacturing processes of PCBs are mainly consisting of six steps: schematic, layout, image transfer, etching, drilling, soldering, and assembling. At the beginning of the design cycle, it is important to estimate the time to complete each step accurately, based on many factors such as required parts, approximate board size and shape, and a rough sketch of schematics. Current approach uses Multiple Linear Regression Technique (MLRT) for model development. However, the need for accurate predictive models continues to grow as the technology becomes more advanced. The main objective of this study is to analyze a large volume of historical PCB design data, extract some important variables, and develop predictive time models based on the extracted variables using Rough Set approach of Data Mining. Being unique and useful in solving machining operation related problems, the approach can derive decision rules and identify the most significant features simultaneously. This paper forms the basis for solving many other similar problems that occur in manufacturing and service industries.
Subject Area
Industrial engineering
Recommended Citation
Nadackal, Alexander Joseph, "Modeling complex manufacturing process activities using data mining: A rough set approach" (2007). ETD Collection for University of Texas, El Paso. AAI1444093.
https://scholarworks.utep.edu/dissertations/AAI1444093