• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.14 no.3
• /
• pp.1-7
• /
• 2005

The prediction for tool life is one of the most important factors for increasing reliability, stability, and productivity of manufacturing system. This paper deals with a tool life prediction method in view of reliability assessment for cutting tools. In this study, flank wear was focused among multi-factors deciding the tool wear state. First, tool life was predicted by correlation between flank wear and cutting time, based on the extended Taylor tool life equation of turning, including parameters of cutting speed, feed rate, and cutting depth. Second, each of cutting conditions of end-milling was equivalently converted to apply ball end-mill data to the extended Taylor equation. The web-based prediction program for tool life was developed as one of reliability assessment programs for machine tools.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.5
• /
• pp.98-107
• /
• 1995

The in-process detection of drill wear and breakage is one of the most importnat technical problems in unmaned machining system. In this paper, the monitoring system is developed to monitor abnormal drilling states such as drill breakage, drill wear and unstable cutting using motor current. Drill breakage is detected by level monitoring. Tool wear is classified by fuzzy pattern recognition. The key feature for classification of tool wear is the estimated flank wear which is calculated by the proposed flank wear model. The characteristic of the model is not sensitive to the variation of cutting conditions but is sensitive to drill wear state. Unstable cutting states due to the unsmooth chip disposal and the overload are monitored by the variance/mean ratio of spindle motor current. Variance/mean ratio also includes the information about the prediction of drill wear and drill breakage. The evaluation experiments have shown that the developed system works very well.