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Research Papers

Multiobjective Optimization of the Heating Process for Forging Automotive Crankshaft

[+] Author and Article Information
Hong-Seok Park

Mem. ASME
Laboratory for Production Engineering,
School of Mechanical and
Automotive Engineering,
University of Ulsan,
San 29, Mugeo 2-dong,
Namgu, Ulsan 680-749, South Korea
e-mail: phosk@ulsan.ac.kr

Xuan-Phuong Dang

Faculty of Mechanical Engineering,
Nha Trang University,
2 Nguyen Dinh Chieu Str.,
Nha Trang City, Khanh Hoa Province 650000, Vietnam
e-mail: phuongdx@ntu.edu.vn

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received August 31, 2014; final manuscript received February 8, 2015; published online March 2, 2015. Assoc. Editor: Gracious Ngaile.

J. Manuf. Sci. Eng 137(3), 031011 (Jun 01, 2015) (8 pages) Paper No: MANU-14-1454; doi: 10.1115/1.4029805 History: Received August 31, 2014; Revised February 08, 2015; Online March 02, 2015

This paper presents potential approaches that increase the energy efficiency of an in-line induction heating system for forging of an automotive crankshaft. Both heat loss reduction and optimization of process parameters are proposed scientifically in order to minimize the energy consumption and the temperature deviation in the workpiece. We applied the numerical multiobjective optimization method in conjunction with the design of experiment (DOE), mathematical approximation with metamodel, nondominated sorting genetic algorithm (GA), and engineering data mining. The results show that using the insulating covers reduces heat by an amount equivalent to 9% of the energy stored in the heated workpiece, and approximately 5.8% of the energy can be saved by process parameter optimization.

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Figures

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Fig. 1

In-line induction heating of a long steel bar prior to hot forging an automotive crankshaft

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Fig. 2

The manufacturing process of the automotive crankshaft

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Fig. 3

The proposed thermal insulating covers in the in-line induction heating system [8]

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Fig. 4

A framework of automatic simulation-based and metamodel-based optimization

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Fig. 5

Algorithm of metamodel-based optimization and systematic procedure for optimizing the heating process

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Fig. 6

The structure of the coupling electromagnetic and thermal analysis in the induction heating

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Fig. 7

Heating strategy that divides the heaters into three groups fed by independent power supplies

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Fig. 8

Circuit-coupled FEM model of induction heating simulation

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Fig. 9

The graphical relations between the factors (frequency 1, frequency 2, voltage) and responses (energy efficiency and temperature deviation): (a) RBF model and (b) RSM model

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Fig. 10

Pareto plot for (a) response efficiency and (b) response temperature deviation

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Fig. 11

Main effects for (a) efficiency and (b) temperature deviation

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Fig. 12

Engineering data mining and Pareto plot used to determine the optimum process parameters

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