Cutting Condition Decision Support System Using Data Mining-Application of Life Cycle Assessment on Estimation of Cutting Conditions-

Abstract

A CAM system can only calculate the tool passing that followed to be used to control the tools for creating the material mold shape designed by using only a CAD system. However, the chosen tools and cutting conditions depend on the expert engineer’s knowledge and experience, because expert engineers manufacture products by way of trial and error until they obtain the appropriate cutting conditions. We previously proposed data mining methods to make decisions about which end-milling conditions to use on the basis of the catalog data. Even inexperienced engineers can instantly decide on stable cutting conditions by a using cutting conditions decision support system. Thus, in this study, we investigate the different cutting conditions of the tool and the power consumption to evaluate the utility of our cutting conditions decision support system. The environmental burdens from the viewpoint of global warming were quantitatively evaluated using LCA (Life Cycle Assessment). We cut hardened die steel JIS SKD61 under three kinds of cutting conditions: catalog conditions, mined conditions and expert engineer conditions. The cumulative environmental burden was the lowest under the expert condition, which indicates that this condition has the most usefulness. However several trial-and-error processes must be repeated in order to reach the expert condition. We designed an index model of the environmental burden in the technical mastering process under this condition. The results show that unskilled engineers could decrease the cumulative environmental burden by working under the mined condition in the initial stage. Recommending the use of the mined condition in the initial stage is therefore considered best.