Abstract
Development of automated machining robots has gained momentum with many well-known processes realizing automation to improve quality and increase efficiency. However, grinding process is difficult to automate. High temperature at the contact between the grinding wheel and the workpiece is the source of thermal deformation and phase transformations which can lead to thermal damage and affect surface integrity. This study aims to achieve a better understanding of the thermal behaviour of the grinding process in order to develop an automated grinding robotic system. We studied the thermal behaviour of Inconel 718 workpiece during the grinding process. Different models have been established previously to characterize the heat generation and dispersion at the interface between grinding wheel and workpiece. The Rowe model is used to calculate the heat escaping from the workpiece, by convection, and convection with the grinding wheel in order to obtain the heat remaining in the workpiece. Once the heat source intensity is calculated, Jaeger’s model for moving heat sources is used to calculate the evolution of the temperature along the length of a bar being ground and along the depth in steady state. A numerical model is created using ABAQUS to take into account non-linearity such as the temperature dependence of the parameters and complex boundary conditions. An experiment is carried out on an Inconel 718 bar to compare with the analytical and numerical results. The results show that the boundary condition and the temperature dependence properties of Inconel 718 cannot be neglected. The temperature profiles obtained by the numerical model, which includes the boundary condition and temperature dependence properties of material, are more consistent with the experimental results.
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Ren, X., Soulard, B., Wang, J., Xu, Y., Chen, X. (2019). Thermal Analysis of Belt Grinding Process of Nickel-Based Superalloy Inconel 718. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-13-8668-8_3
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DOI: https://doi.org/10.1007/978-981-13-8668-8_3
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