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Research Papers

Stochastic Modeling and Diagnosis of Leak Areas for Surface Assembly

[+] Author and Article Information
Jie Ren, Chiwoo Park

Department of Industrial and
Manufacturing Engineering,
Florida State University,
Tallahassee, FL 32310

Hui Wang

Department of Industrial and
Manufacturing Engineering,
Florida State University,
Tallahassee, FL 32310
e-mail: hwang10@fsu.edu

1Corresponding author.

Manuscript received April 11, 2017; final manuscript received December 10, 2017; published online February 13, 2018. Assoc. Editor: Laine Mears.

J. Manuf. Sci. Eng 140(4), 041011 (Feb 13, 2018) (10 pages) Paper No: MANU-17-1244; doi: 10.1115/1.4038889 History: Received April 11, 2017; Revised December 10, 2017

Assembly through mating a pair of machined surfaces plays a crucial role in many manufacturing processes such as automotive powertrain production, and the mating errors during the assembly (i.e., gaps between surfaces) can cause significant internal leakage and functional performance problems. The surface mating errors are difficult to diagnose because they are not measurable. Current in-plant quality control for surface mating focuses on controlling the surface flatness of each individual part before they are mated, and the mating errors are indirectly evaluated by a pressurized sealing test to check whether any pressure drop occurs. However, it does not provide any clue to engineers about the origins and the root cause of the internal leakage. To address these limitations, this paper presents a pressurized color-tracking method to directly measure internal leak areas. By using the measurements of leak areas and the profiles of surfaces mated as training data along with Hagen–Poiseuille law, this paper develops a novel diagnostic method to predict potential leak areas (leakage paths) given the measurements on the profiles of mating surfaces. The effectiveness and robustness of the proposed method are verified by a simulation study and an experiment. The approach provides practical guidance for the subsequent assembly process as well as troubleshooting in surface machining processes.

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Figures

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Fig. 1

Sealing test demonstrating the insufficiency of qualified individual surface variation. Note: the color/gray scale represents the surface height, by which the dark color refers to a low height value while the bright color is the opposite.

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Fig. 2

An example of surface measurement of different resolution from different metrology system [6]: (a) Coherix ShaPix surface measurement using laser holographic interferometry and (b) CMM measurement

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Fig. 3

Void space of mating surfaces

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Fig. 4

Isolated void areas with no leakage

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Fig. 5

(a) Surface partition and (b) lattice graph of the mating area

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Fig. 6

Framework of the leakage diagnosis

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Fig. 7

Design of a surface mating testbed: (a) mini engine head, (b) mini engine block, and (c) surface assembly (unit: mm)

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Fig. 8

Data acquisition of the testbed: (a) Coherix ShaPix3D metrology system and (b) leakage test

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Fig. 9

Simulated void space and corresponding leak areas: (a) and (b) are training void space data with σ=0,0.05, respectively, (c) is training leak areas, (d) and (e) are test void space data with σ=0,0.05, respectively, and (f) is test leak areas (the color/gray scale bars in (a), (b), (d), and (e) reflect the magnitude of void space)

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Fig. 10

Predicted probabilistic leak areas with different noise levels and their log-likelihood values: (a) predicted leak areas with σ = 0, (b) example of predicted leak areas with σ=0.05, (c) log-likelihood versus increasing noise level (the color/gray scale bars in (a) and (b) reflect the probability of leak at every grid point)

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Fig. 11

Experimental data: (a) surface height measurement of block, (b) surface height measurement of head, and (c) measured leak areas (unit: mm)

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Fig. 12

Subsamples and prediction results: (a) five equal sized subsamples and (b) predicted leak areas (color/gray scale bar reflects the probability of leak) (axial unit: mm)

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