Accurate information about the evolution of the temperature field is a theoretical prerequisite for investigating grinding burn and optimizing the process parameters of grinding process. This paper proposed a new statistical model of equivalent grinding heat source with consideration of the random distribution of grains. Based on the definition of the Riemann integral, the summation limit of the discrete point heat sources was transformed into the integral of a continuous function. A finite element method (FEM) simulation was conducted to predict the grinding temperature field with the embedded net heat flux equation. The grinding temperature was measured with a specially designed in situ infrared system and was formulated by time–space processing. The reliability and correctness of the statistical heat source model were validated by both experimental temperature–time curves and the maximum grinding temperature, with a relative error of less than . Finally, through the FEM-based inversed calculation, an empirical equation was proposed to describe the heat transfer coefficient (HTC) changes in the grinding contact zone for both conventional grinding and creep feed grinding.
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A Statistical Model of Equivalent Grinding Heat Source Based on Random Distributed Grains
Zhenguo Nie,
Zhenguo Nie
Beijing Key Lab of Precision/Ultra-Precision
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mails: zhenguo.nie@me.gatech.edu;
zhenguonie@gmail.com
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mails: zhenguo.nie@me.gatech.edu;
zhenguonie@gmail.com
Search for other works by this author on:
Gang Wang,
Gang Wang
Beijing Key Lab of Precision/Ultra-Precision
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Department of Mechanical Engineering,
Tsinghua University,
Beijing 100084, China
e-mail: gwang@tsinghua.edu.cn
Tsinghua University,
Beijing 100084, China
e-mail: gwang@tsinghua.edu.cn
Search for other works by this author on:
Dehao Liu,
Dehao Liu
Beijing Key Lab of Precision/Ultra-Precision
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
Search for other works by this author on:
Yiming (Kevin) Rong
Yiming (Kevin) Rong
Beijing Key Lab of Precision/Ultra-Precision
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Department of Mechanical
and Energy Engineering,
South University of Science and
Technology of China,
Shenzhen 518055, China
and Energy Engineering,
South University of Science and
Technology of China,
Shenzhen 518055, China
Search for other works by this author on:
Zhenguo Nie
Beijing Key Lab of Precision/Ultra-Precision
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mails: zhenguo.nie@me.gatech.edu;
zhenguonie@gmail.com
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mails: zhenguo.nie@me.gatech.edu;
zhenguonie@gmail.com
Gang Wang
Beijing Key Lab of Precision/Ultra-Precision
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Department of Mechanical Engineering,
Tsinghua University,
Beijing 100084, China
e-mail: gwang@tsinghua.edu.cn
Tsinghua University,
Beijing 100084, China
e-mail: gwang@tsinghua.edu.cn
Dehao Liu
Beijing Key Lab of Precision/Ultra-Precision
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
Yiming (Kevin) Rong
Beijing Key Lab of Precision/Ultra-Precision
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Manufacturing Equipments and Control,
Tsinghua University,
Beijing 100084, China;
Department of Mechanical
and Energy Engineering,
South University of Science and
Technology of China,
Shenzhen 518055, China
and Energy Engineering,
South University of Science and
Technology of China,
Shenzhen 518055, China
1Corresponding author.
Manuscript received September 13, 2017; final manuscript received December 4, 2017; published online March 7, 2018. Assoc. Editor: Y. B. Guo.
J. Manuf. Sci. Eng. May 2018, 140(5): 051016 (13 pages)
Published Online: March 7, 2018
Article history
Received:
September 13, 2017
Revised:
December 4, 2017
Citation
Nie, Z., Wang, G., Liu, D., and Rong, Y. (. (March 7, 2018). "A Statistical Model of Equivalent Grinding Heat Source Based on Random Distributed Grains." ASME. J. Manuf. Sci. Eng. May 2018; 140(5): 051016. https://doi.org/10.1115/1.4038729
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