An intelligent data mining system for drop test analysis of electronic products

Abstract

Drop testing is one common method for systematically determining the reliability of portable electronic products under actual usage conditions. The process of drop testing, interpreting results, and implementing design improvements is knowledge-intensive and time-consuming, and requires a great many decisions and judgments on the part of the human designer. To decrease design cycles and, thereby, the time to market for new products, it is important to have a method for quickly and efficiently analyzing drop test results, predicting the effects of design changes, and determining the best design parameters. Recent advances in data mining have provided techniques for automatically discovering underlying knowledge from large amounts of experimental data. In this paper, an intelligent data mining system named decision tree expert (DTE) is presented and applied to drop testing analysis. The rule induction method in DTE is based on the C4.5 algorithm. In our preliminary experiments, concise and accurate conceptual design rules were successfully generated from drop test data after incorporation of domain knowledge from human experts. The data mining approach is a flexible one that can be applied to a number of complex design and manufacturing processes to reduce costs and improve productivity.