K. Ramamurthi et Cl. Hough, INTELLIGENT REAL-TIME PREDICTIVE DIAGNOSTICS FOR CUTTING TOOLS AND SUPERVISORY CONTROL OF MACHINING OPERATIONS, Journal of engineering for industry, 115(3), 1993, pp. 268-277
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.