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research-article

Stochastic Modeling and Diagnosis of Leak Areas for Surface Assembly

[+] Author and Article Information
Jie Ren

Department of Industrial and Manufacturing Engineering, Florida State University, Tallahassee, Florida 32310
jr14r@my.fsu.edu

Chiwoo Park

Department of Industrial and Manufacturing Engineering, Florida State University, Tallahassee, Florida 32310
cpark5@fsu.edu

Hui Wang

Department of Industrial and Manufacturing Engineering, Florida State University, Tallahassee, Florida 32310
hwang10@fsu.edu

1Corresponding author.

ASME doi:10.1115/1.4038889 History: Received April 11, 2017; Revised December 10, 2017

Abstract

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.

Copyright (c) 2017 by ASME
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