Xiaohong Ding
University of Shanghai for Science and Technology
7 Papers
Xiaohong Ding is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Machining & Machine tool. The author has an hindex of 5, co-authored 7 publications.
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Papers
Design of an internally cooled turning tool based on topology optimization and CFD simulation
TL;DR: In this paper, an internally cooled turning tool is designed based on topology optimization and CFD simulation, followed by solid isotropic material with penalization (SIMP) model imported in mechanical and heat conduction analyzing models for the tool flow channel design and optimization.
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Design of a Modular Green Closed Internal Cooling Turning Tool for Applications
TL;DR: The numerical simulation results demonstrate that the newly designed internal cooing turning tool could effectively decrease the tool temperature, and have potentially great significance in improving tooling performance, as well as achieving environmental friendly, economical and sustainable machining.
22
Structural dynamic design optimization and experimental verification of a machine tool
TL;DR: The Adaptive Growth Method which is based on the growth mechanism of natural branch systems is adopted to design the inner stiffener layout of structures, and an optimization strategy for the holistic machine tool utilizing dynamic sensitivity analysis is studied.
22
Thermal error control method based on thermal deformation balance principle for the precision parts of machine tools
Zeji Ge,Xiaohong Ding +1 more
TL;DR: In this paper, a thermal error control method based on the thermal deformation balance principle is suggested to control the thermal error of a precision machine tool's functional parts, which negatively affects processing precision.
18
Tolerance analysis of the volumetric error of heavy-duty machine tool based on interval uncertainty
TL;DR: It is found that under the given computational condition, the tolerance analysis based on the interval uncertainty can accurately evaluate the variation range of the volumetric error.
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