An Online Tool Temperature Monitoring Method Based on Physics-Guided Infrared Image Features and Artificial Neural Network for Dry Cutting

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.