Quality improvement by using grey prediction tool compensation model for uncoated and TiAlCN-coated tungsten carbide tools in depanel process of memory modules

Abstract

Small outline dual in-line memory modules (SO-DIMM) are the memory modules used in notebook computers. This study investigates the SO-DIMM accuracy and tool life using an uncoated and TiAlCN-coated tungsten carbide tool in the depanel process. During the cutting process, the tool operating time of a TiAlCN-coated tool can be extended and the surface quality of SO-DIMM at the cutting edge is better than uncoated tools based on EDS (energy dispersive spectrometer) observation. In the milling process, tool wear is a serious problem for new tools and to solve the problem of dimension variation, this study proposes the use of grey prediction tool compensation to offset the variation and enhance the quality of SO-DIMM. According to the process capability indices within the experimental scope, the quality of memory modules using uncoated and TiAlCN-coated tools using grey prediction tool compensation were shown to improve both its Ca and Cpk values. Thus, the grey prediction tool compensation method has really proven to be workable and can improve the quality of SO-DIMM.