INTELLIGENT REAL-TIME PREDICTIVE DIAGNOSTICS FOR CUTTING TOOLS AND SUPERVISORY CONTROL OF MACHINING OPERATIONS

Machining economics may be improved by automating the replacement of c utting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligen t real-time diagnostic system is developed and applied towards that ob jective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve th e diagnostic problem in real-time applications. A formal methodology i s outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling ma chine and evaluated on-line. The on-line response is well within the d esired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear i s not diagnosed in a timely manner, the system predicts wear induced f ailure and vice versa. By diagnosing at least one of the two failure m odes, the system is able to prevent any abrupt failure of the drill du ring machining.