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.

Issue Section:
Research Papers
Topics:
Optimization, Turning, Artificial neural networks, Cutting, Dimensions, Engineering simulation, Feedback, Feedforward control, Finishes, Finishing, Machining, Manufacturing, Sensors, Simulation, Vibration
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