Sensing and Compensating the Thermal crippled of a Computer-numerical-control Grinding Machine Using a Hybrid Deep-learning Neural Network Scheme

Abstract

Thermal error plays a fatalist part in the machining fidelity of computer-numerical control (CNC) tool device. In Past, three ways had been suggested to overcome thermal error complication: prevention, restraint, and compensation. The first two ways may beperformed in the initial design stage. The last one includes the challengeable features of case by-case simulation of cutting paths, searching for characteristic temperature points, thermal deformation measurement, and establishing an accurate thermal model. Different from most of the previous studies concerning mathematical thermal models, which have many restrictions and disadvantages, in this study, we propose a novel hybrid thermal error modelling scheme of the Grey system theorem and deep-learning neural network. Specifically, a linear-guideway grinding machine, never seen in previous thermal-error-compensation-related studies, wasc hosen as the target to identify the usefulness of our proposed scheme. Results show that theproposed hybrid model has a comprehensive prediction ability of thermal behavior for the target CNC grinding machine.