Artificial Neural Networks for Controlling the Temperature of Internally Cooled Turning Tools

Frank Wardle, Timothy Minton, Saiful Bin Che Ghani, Paul Fϋrstmann, Martin Roeder, Sebastian Richarz, Fiona Sammler

Brunel University, London, England.
Technische Universit?t, Berlin, Germany.
UPM Ltd., Swindon, England.

DOI: 10.4236/mme.2013.32A001                

Abstract

By eliminating the need for externally applied coolant, internally cooled turning tools offer potential health, safety and cost benefits in many types of machining operation. As coolant flow is completely controlled, tool temperature measurement becomes a practical proposition and can be used to find and maintain the optimum machining conditions. This also requires an intelligent control system in the sense that it must be adaptable to different tool designs, work piece materials and machining conditions. In this paper, artificial neural networks (ANN) are assessed for their suitability to perform such a control function. Experimental data for both conventional tools used for dry machining and internally cooled tools is obtained and used to optimise the design of an ANN. A key finding is that both experimental scatter characteristic of turning and the range of machining conditions for which ANN control is required have a large effect on the optimum ANN design and the amount of data needed for its training. In this investigation, predictions of tool temperature with an optimised ANN were found to be within 5°C of measured values for operating temperatures of up to 258°C. It is therefore concluded that ANN’s are a viable option for in-process control of turning processes using internally controlled tools.

Keywords

Control Systems; In-Process Control; Artificial Neural Network; Machine Tools

Conflicts of Interest

The authors declare no conflicts of interest.

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