Temperature field sensing of a thin-wall component during machining: Numerical and experimental investigations

被引:65
作者
Elsheikh, Ammar H. [1 ,2 ]
Guo, Jiajie [1 ]
Huang, Yang [1 ]
Ji, Jingjing [1 ]
Lee, Kok-Meng [1 ,3 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Dig Mfg & Equip Tech, Sch Mech Sci & Engn, Wuhan 430074, Hubei, Peoples R China
[2] Tanta Univ, Dept Prod Engn & Mech Design, Tanta 31527, Egypt
[3] Georgia Inst Tech, Woodruff Sch Mech Engn, Atlanta, GA 30332 USA
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
Heat conduction; Thin-wall plate; Temperature field reconstruction; Modal expansion; Machining; HEAT-CONDUCTION PROBLEM; TOOL-CHIP INTERFACE; THERMAL DEFLECTION; TRANSIENT TEMPERATURE; INVERSE PROBLEM; CIRCULAR PLATE; CUTTING-TOOL; STEADY-STATE; FLUX; GENERATION;
D O I
10.1016/j.ijheatmasstransfer.2018.06.006
中图分类号
O414.1 [热力学];
学科分类号
摘要
Thermal dynamics of hard-to-machined components during manufacturing contributes to micro defects and residual stresses in final products and overheating on machine tools, where temperature plays a critical role in the study of the tool-workpiece interface. However, typical temperature sensing approaches are limited in manufacturing due to their dependence on controlled environments without blockages and cutting fluids/chips, complicated algorithms with long computation time, and knowledge of heat source intensity that is hard to estimate. This paper proposes a temperature field reconstruction (TFR) method as a real-time and online approach to investigate the thermal dynamics of a thin-wall disk-like workpiece (WP) during a turning process. Formulating in a modal expansion with physical laws, the method decouples the temperature field into products of spatially-distributed temperature mode shapes and time varying modal coefficients that are determined from a finite number of nodal measurements. The TFR method is demonstrated and verified with simulated measurements in finite element analysis, and an illustrative application to TFR during machining is presented to justify its ability for real-time computing and online sensing in manufacturing. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:935 / 945
页数:11
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