Experimental verification of micro end-milling condition decision methodology using data-mining system

A growing number of requirements are being placed on micro fabrication using micro end mills with high operating degrees-of-freedom. In particular, when the minor diameter of the end mill is 1.0 mm or less, the handling of tools becomes difficult because of the influence of the characteristic size effect and bending of the cutting edge. Furthermore, it is hard for engineers to derive the cutting conditions that can serve as indexes in the early stage of micro end-milling. To solve this problem, in this research, on a basis of workpiece material-characteristics and tool shape parameters, a system that can make instantaneous decisions was developed by applying data mining techniques together with non-hierarchical and hierarchical clustering methods on micro end-mill catalog data. Milling experiments using cemented carbide square micro end-mill were carried out to investigate the practicability of derived mining conditions under slotting of A7075 (JIS). As a result, it turned out that setting of the amount of axial depth of cut resulting from the rigid fall of cutting edge was important for stability of surface integrity, and the acquired knowledge needs to feed back to the proposed supporting system.