On-Line Optimization of the Turning Process Using an Inverse Process Neurocontroller

[+] Author and Article Information
R. Azouzi, M. Guillot

Mechanical Engineering Department, Laval University, Canada, G1K-7P4

J. Manuf. Sci. Eng 120(1), 101-108 (Feb 01, 1998) (8 pages) doi:10.1115/1.2830085 History: Received January 01, 1993; Online January 17, 2008


This paper presents a feedback neurocontrol scheme that uses an inverse turning process model to synthesize optimal process inputs. The inverse process neurocontroller is implemented in a multilayer feedforward neural network. On-line adjustments of feed rate and cutting speed parameters are carried out based on a cost/quality performance index, estimated from force and vibration sensor measurements. Both non-adaptive and adaptive neurocontrol schemes are considered. The simulations and experimental investigations presented herein demonstrated the effectiveness of neural networks for controlling and optimizing turning operations. Applied to single point turning of a typical finishing cut, the final dimensions and surface finishes were found to be better by 40 and 80 percent respectively, while productivity was increased by 40 percent over the conditions proposed in machining data handbooks. This approach is also applicable to several other manufacturing processes.

Copyright © 1998 by The American Society of Mechanical Engineers
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