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A drop-impact reliability assessment of mobile display modules using a statistical modeling approach

Abstract

Since drop-impact is the most frequent cause of failure of mobile devices and the display is the major channel for users to interact with mobile devices, the reliability of the display module by drop-impact is one of the most important concerns for both manufacturers and customers. In this paper, we propose- a drop-impact reliability assessment method for the mobile display module using a statistical modeling approach. First, the general likelihood functions that consider various censored data are proposed to obtain accurate estimates. Second, under the constraints of the test budget and the sample size, the optimization problem for minimizing the mean squared error of the target of interest with the decision variables was proposed. The proposed method was applied to the real application example to determine the most reliable mobile display module design.

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Abbreviations

n :

Sample size

h 0 :

Initial drop height

Δh :

Increasing interval of height

h m :

Maximum allowable height

x :

Failure height of a sample

f WE :

PDF of the Weibull distribution

f IG :

PDF of the inverse-Gaussian distribution

\({{\mathcal L}_{\rm{c}}}\) :

Conventional likelihood function

\({{\mathcal L}_{\rm{g}}}\) :

General likelihood function

x p :

100p-th percentile of the failure distribution

ⅅ:

Decision variable vector (n,h0, Δh)

MSE:

Mean squared error from the true value

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Acknowledgments

This work is supported by research fund from Chosun University, 2017.

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Correspondence to Suk Joo Bae.

Additional information

Seong-Joon Kim received the B.S. and Ph.D. degrees in Industrial Engineering, both from Hanyang University, Seoul, South Korea, in 2006 and 2013, respectively. He worked as a Senior Research Engineer in Data Analytics Team at Doosan Heavy Industries & Construction, Seoul, South Korea. He is currently an Assistant Professor in the Department of Industrial Engineering at Chosun University, Gwangju, South Korea. His research interests cover design and reliability analysis of accelerated degradation and life tests. He also has research interests in data science and machine learning applications to large-scale system for PHM (prognostics and health management).

Daeil Kwon received the Bachelor’s degree in Mechanical Engineering from POSTECH, South Korea, and the Ph.D. degree in Mechanical Engineering from the University of Maryland, College Park, MD, USA. He was a Senior Reliability Engineer with Intel Corporation, Chandler, AZ, USA, where he developed use condition-based reliability models and methodologies for assessing package and system reliability performance. He is currently an Associate Professor with Sungkyunkwan University, Suwon, South Korea. His research interests are focused on prognostics and health management of electronics, reliability modeling, and use condition characterization.

Suk Joo Bae received the Ph.D. degree from the School of Industrial and Systems Engineering, Georgia Institute of Technology, in 2003. He is currently a Professor with the Department of Industrial Engineering, Hanyang University, Seoul, South Korea. He has served as a Reliability Engineer with Samsung SDI, South Korea, from 1996 to 1999. His research interests are centered on reliability evaluation of light displays and nanodevices via accelerated life and degradation testing, statistical robust parameter design, and process control for large-volume on-line processing data. He is a member of the INFORMS, ASA, and IMS. He is currently an Associate Editor of the IEEE Transactions on Reliability.

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Kim, Sj., Kwon, D. & Bae, S.J. A drop-impact reliability assessment of mobile display modules using a statistical modeling approach. J Mech Sci Technol 34, 3945–3955 (2020). https://doi.org/10.1007/s12206-020-2204-y

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  • DOI: https://doi.org/10.1007/s12206-020-2204-y

Keywords

  • Drop-impact
  • Mobile display module
  • Reliability assessment
  • Design of experiment
  • Statistical modeling
  • Weibull distribution
  • Inverse-Gaussian distribution