An Online Tool Temperature Monitoring Method Based on Physics-Guided Infrared Image Features and Artificial Neural Network for Dry Cutting
An Online Tool Temperature Monitoring Method Based on Physics-Guided Infrared Image Features and Artificial Neural Network for Dry Cutting
复制标题
基于物理引导红外图像特征和人工神经网络的干切削刀具温度在线监测方法
DOI：
10.1109/tase.2018.2826362
复制
发表时间：
2018-10-01
影响因子：
5.6
通讯作者：
Lin, Chun-Yeon
中科院分区：
文献类型：
--
作者：
Lee, Kok-Meng;Huang, Yang;Lin, Chun-Yeon
本文提出了一种有效的方法，重建周围的刀具/芯片界面的温度场的红外（IR）热图像，在线监测刀具的内部峰值温度。工具温度场被分为两个区域，即，远场用于求解工具和环境温度之间的传热系数，和近场，其中人工神经网络（ANN）被训练以考虑摩擦接触界面处的未知热变化。讨论了从红外图像中提取基于物理的特征点作为人工神经网络输入的方法。图像分辨率，特征选择，芯片闭塞，接触热变化，和测量噪声上估计的接触温度的影响进行了数值和实验分析。所提出的方法已被验证，通过比较人工神经网络估计的表面温度对“真值”实验获得的高分辨率红外成像仪上定制设计的试验台，以及使用有限元分析的数值模拟。温度监测方法的概念的可行性证明了工业车床车削中心与商业刀片。
This paper presents an efficient method, which reconstructs the temperature field around the tool/chip interface from infrared (IR) thermal images, for online monitoring the internal peak temperature of the cutting tool. The tool temperature field is divided into two regions; namely, a far field for solving the heat-transfer coefficient between the tool and ambient temperature, and a near field where an artificial neural network (ANN) is trained to account for the unknown heat variations at the frictional contact interface. Methods to extract physics-based feature points from the IR image as ANN inputs are discussed. The effects of image resolution, feature selection, chip occlusion, contact heat variation, and measurement noises on the estimated contact temperature are analyzed numerically and experimentally. The proposed method has been verified by comparing the ANN-estimated surface temperatures against "true values" experimentally obtained using a high-resolution IR imager on a custom-designed testbed as well as numerically simulated using finite-element analysis. The concept feasibility of the temperature monitoring method is demonstrated on an industrial lathe-turning center with a commercial tool insert.