In this study, we applied data mining methods to printed circuit board (PCB) tool catalogs (defined as catalog mining), which contain a wealth of information on machining, to search for machining conditions, formulate hypotheses, and ultimately discover and construct new knowledge useful for drilling holes in PCBs. In particular, this paper focuses on classification by clustering methods. Application of the overlapping k-means method to the relationship between spindle speed and tool peripheral speed, common elements were found in all clusters. Hole drilling experiments under common conditions showed that the number of holes drilled exceeded the number of holes recommended in the catalog, indicating that these cutting conditions may provide representative stable drilling conditions.

Silicon carbide (SiC) is one of the main materials used for power semiconductor devices. In this study, 4-inch SiC wafers were ground, and the machining characteristics were evaluated by the grain approach angle. The grinding conditions for 6-inch SiC wafers were selected based on the results. Finally, the grinding results for 6-inch SiC wafers were compared to those for 4-inch SiC wafers. The appropriate grinding conditions could be selected for SiC wafers with larger diameters using the grain approach angle. The arithmetic mean height (Sa) was 2.2 nm for wafers of all outer diameters.