Predicting defects in disk drive manufacturing: A case study in high-dimensional classification

The authors consider the application of several computationally intensive classification techniques to disk drive manufacturing quality control. This application is characterized by very high dimensions, with hundreds of features, and tens of thousands of cases. The efforts were directed toward a search for knowledge that would assist the engineers in providing another increment of performance to the already highly performing knowledge-based system. Two principal issues are addressed. The issues are whether a very expensive testing process can be eliminated while still maintaining high quality throughput in disk drive manufacturing, and whether the manufacturing process can be made more efficient by identifying bad disk drives prior to the expensive testing. Preliminary results indicate that although the expensive testing cannot be completely eliminated, a fraction of the disk drives can be determined to be faulty prior to further testing. This detection may improve the throughput of the manufacturing line