Development of a guideline proposal system for correcting cutting conditions based on the overhang length of ball end-mills

Abstract

In the field of die and mold machining, determining appropriate cutting conditions is crucial. Factors such as tool geometry, machining path, work material characteristics, machining efficiency, and finishing accuracy must be taken into consideration. However, the current method of determining cutting conditions relies heavily on the intuition and experience of skilled engineers, and there is a need for a system to replace such knowledge. One of the critical factors affecting machining accuracy and efficiency is the tool overhang length, which is directly related to tool geometry. Unfortunately, there is no clear guideline for its determination. In a previous study, researchers developed a system to quickly derive cutting conditions using a data mining method and Random Forest Regression (RFR) applied to a tool catalog database. In this study, we constructed a new cutting condition compensation system based on the existing model, which accounts for the tool overhang length. The results of cutting experiments under high aspect ratio overhang lengths confirm that the correction coefficients proposed by the system are significant.

Keywords

• data mining
• cutting conditions
• machine learning
• random forest regression
• ball end-mill
• tool overhang length