An adaptive fuzzy control system to maximize rough turning productivity and avoid the onset of instability

This paper presents a new method to improve cutting efficiency for steel rough turning. To date, most efforts aimed at improving productivity during cutting operations have concentrated on optimizing material handling to and from the machinery. Here, the focus is on improving the efficiency of the turning operation itself. The approach is to control feed rate to raise machine power to a maximum safe level while avoiding the onset of cutting instability. The measure of machine power comes directly from the spindle motor and is held below the cutting machine??s power capacity. Detecting the onset of instability relies on interpreting data that come from installed instrumentation. A fuzzy inference system processes the inputs and makes the final control decisions. The prototype system was tuned using data collected in a variety of cutting situations. Subsequent testing of the tuned control system showed that it was capable of successfully maximizing power usage while still avoiding the onset of instability.