It is very important to use a reliable and inexpensive sensor to obtain useful information about manufacturing processing, such as cutting force for monitoring automated machining. In this paper, the feed-cutting force is estimated using inexpensive current sensors installed on the ac servomotor of a computerized numerical control (CNC) turning center, with the results applied to the intelligent tool wear monitoring system. The mathematical model is used to disclose the implicit dependency of feed-cutting force on feed-motor current and feed speed. Afterwards, a neurofuzzy network is used to identify the cutting force with current measurement only. This hybrid math-fuzzy approach will reduce the modeling uncertainty and measurement cost. Finally, the estimated cutting force is applied in the tool-wear monitoring process. Successful experiments demonstrate robustness and effectiveness of the suggested method in the wide range of tool-wear monitoring applications.

A new method for workpiece error analysis and compensation in turning is introduced. It is known that the workpiece error consists of two parts: machine tool error (including the geometric error and thermal-induced error) and cutting induced error. The geometric error of the machine tool is independent on machining operation and, hence, can be measured o. -line using a

In this paper, an effective algorithm based on improved time-domain averaging is proposed to detect tool flute breakage during end milling using feed-motor current signatures. The algorithm proposed is demonstrated to be effective in detecting tool flute breakage in real time through a series of milling experiments, and is also demonstrated to be insensitive to the effects for transients, such as cutter runout, entry/exit cuts, and noise in the feed-motor current signals. Results indicated that the approach showed excellent potential for practical, on-line application for tool flute breakage detection during end milling.