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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. Note to Practitioners—The internal tool-temperature field around the tool/chip interface during cutting offers essential information to monitor tool-wear and ensure surface-quality particularly in finishing cuts, which is difficult to be monitored because of the stringent real-time requirements (including low cost and high accuracy) in harsh working environments. This paper presents a potentially low-cost solution that combines noncontact surface temperature measurements with high-fidelity physics-based computational models for monitoring the internal peak temperature at the tool/chip frictional contact during cutting. This physics-based method requires only a small number of preselected features, and uses thermal isotherms and streamlines to detect and substitute any occlusions in the IR images. The method does not rely on high-resolution (HR) images to infer the steep temperature gradient near the tool-tip where the peak temperature occurs, and thus can be implemented with a relatively low-cost IR imager. Unlike traditional least-square methods that bases solutions to a heat-conduction partial differential equation (PDE), the ANN is trained with precomputed physics-based models based on a novel dual-field (far-field and near-field) approach. The ANN-based method not only eliminates the need to solve the time-demanding PDE in real time, but also effectively accounts for parameter variations due to the uncertain frictional heat-fluxes at the contact interface during cutting.