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Journal Of Intelligent & Robotic Systems 12
(2):103-125, 1995. © Kluwer Academic Publishers
Learning to Monitor a Machine ToolMieczyslaw M.
Kokar Northeastern University, Department of Industrial
Engineering, Snell Building, Boston MA 02115, U.S.A.
Jerzy
Letkowski Western New England College
Thomas F.
Callahan University Research Engineers and
Associates
Abstract This paper deals with the issue
of automatic learning and recognition of various conditions of a machine
tool. The ultimate goal of the research discussed in this paper is to
develop a comparehensive monitor and control (M&C) system that can
substitute for the expert machinist and perform certain critical
in-process tasks to assure quality production. The M&C system must
reliably recognize and respond to qualitatively different behaviours of
the machine tool, learn new behaviors, respond faster than its human
counterpart to quality threatening circumstances, and interface with an
existing controller. The research considers a series of face-milling
anomalies that were subsequently simulated and used as a first step
towards establishing the feasibility of employing machine learning as an
integral component of the intelligent controller. We address the question
of feasibility in two steps. First, it is important to know if the process
models (dull tool, broken tool, etc.) can be learned (model learning). And
second, if the models are learned, can an algorithm reliably select an
appropriate model (distinguish between dull and broken tools) based on
input from the model learner and from the sensors (model selection). The
results of the simulation-based tests demonstrate that the milling-process
anomalies can be learned, and the appropriate model can be reliably
selected. Such a model can be subsequently utilized to make compensating
in-process machine-tool adjustments. In addition, we observed that the
learning curve need not approach the 100% level to be
functional.
Keywords Machine learning, reinforcement
learning, intelligent control, machine tool, tool monitoring, metal
cutting, manufacturing
ISSN
0921-0296
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