Knowledge-based linguistic equations for defect detection through functional testing of printed circuit boards
Increasing globalization of the economy is imposing tough challenges to manufacturing companies. The ability to produce highly customized products, in order to satisfy market niches, requires the introduction of new features in automation systems. Flexible manufacturing processes must be able to handle unforeseen events, but their complexity makes the supervision and maintenance task difficult to perform by human operators. This paper describes how linguistic equations (LE), an intelligent method derived from Fuzzy Algorithms, has been used in a decision-helping tool for electronic manufacturing. In our case the company involved in the project is mainly producing control cards for the automotive industry. In their business, nearly 70% of the cost of a product is material cost. Detecting defects and repairing the printed circuit boards is therefore a necessity. With an ever increasing complexity of the products, defects are very likely to occur, no matter how much attention is put into their prevention. Therefore, the system described in this paper comes into use only during the final testing of the product and is purely oriented towards the detection and localization of defects. Final control is based on functional testing. Using linguistic equations and expert knowledge, the system is able to analyze that data and successfully detect and trace a defect in a small area of the printed circuit board. If sufficient amount of data is provided, self-tuning and self-learning methods can be used. Diagnosis effectiveness can therefore be improved from detection of a functional area towards component level analysis.
knowledge linguistic equations defect detection diagnosis fuzzy logic