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

• 作者: Fiona Sammler Frank Wardle Martin Roeder Paul F?rstmann Saiful Bin Che Ghani Sebastian Richarz Timothy Minton
• 来源期刊: 现代机械工程(英文)
• 学科: 医学
• 关键词: CONTROL Systems In-Process CONTROL Artificial NEURAL Network MACHINE TOOLS
• 年，卷（期）: 2013,（2）
• 页码范围: 1-10
• 页数: 10页
• 分类号: R73

摘要

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.