Abstract This paper describes how Linguistic Equations, an intelligent method derived from Fuzzy Algorithms, have been used in a decision-helping tool adapted to the specific needs of electronics manufacturing. In our case the company involved in the project, PKC Group, is mainly producing control cards for the telecommunication and automotive industry. In their business, nearly 70 percent 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. The work focused therefore on defect detection during the final testing of the product. The approach is based on experience using intelligent methods such as Fuzzy Logic or Linguistic Equations in fault diagnosis. An intelligent system based on expert knowledge was developed for analyzing test data. This analysis emphasizes localization of the defective components more than possible causes of those defects. Expert knowledge was essential for the development of the system as the number of defects is too low for a data-based approach. According to the first results, the system is successful for new products, even in the ramp-up stage. On the other hand, the underlying methodology provides techniques for tuning the tool parameters when amount of testing data increases. Diagnosis effectiveness can therefore be improved from detection of a functional area towards component level analysis.

This report is engineer oriented, which means that it has to stay simple and accessible to people without research background. In the first part of this report, we will give a short description of the different methods used in the project with exemples from the case study. The second part concentrates on what has been implemented in the company, what were the needs and what are the improvements that are expected from this work.