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• W4309777197 abstract "Generally, the accuracy stability of a precision motorized spindle unit is influenced by its structural thermal balance behaviors (power matching degree of spindle structural heat generation–dissipations). For the accurate analysis of this spindle heat transfer behaviors based on temperature detections, this paper describes a method constructed with the numerical simulation technology and a proposed genetic algorithm-extreme learning machine (GA-ELM) algorithm. Firstly, the heat–fluid–solid coupling FE transient model is established to simulate spindle thermal behaviors, determinations of its thermal loads/boundary conditions are initially based on empirical calculations. Secondly, according to spindle temperature detections, these initial values are applied to thermal simulations and corrected by GA to make simulated spindle temperatures gradually approach detections. Specially, ELM is adopted to estimate the functional relationships from the parent population to the child population generated by genetic operators with increasing fitness values, and the trained ELM model is utilized to ensure the GA faster convergence. Eventually, based on the corrected thermal load/boundary condition values and simulation results, the time-varying power matching conditions of spindle structural heat generation–dissipation are analyzed. This study provides a theoretical basis for the optimization and promotion of spindle structural design and coolant control strategy." @default.
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• W4309777197 date "2022-11-21" @default.
• W4309777197 modified "2023-10-18" @default.
• W4309777197 title "Numerical analysis onto thermal balance behaviors of motorized spindle unit" @default.
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• W4309777197 doi "https://doi.org/10.1007/s00170-022-10399-6" @default.
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