Reliability analysis considering dynamic material local deformation

ABSTRACT Material deformation is one of the major causes of material failure. In a dynamic deformation process, local deformation—defined as the displacement of various local points on a material—essentially determines the failure. Most existing studies on material reliability are conducted based on either the failure time or the degradation data. The studies do not consider the dynamic local deformation of materials and often are not efficient enough to model the failure mechanism. In this article, we develop reliability analysis by using information contained in the dynamic local deformation of materials in a tensile process. Specifically, a new multivariate general-path model is proposed to describe the deformation process. A two-stage method is developed to estimate the model parameters and overcome the computational complexity. Based on the proposed model, reliability analyses are conducted for various cases of material deformation paths. A simulation study is implemented to verify and validate the developed methods. Physical experiments are designed and conducted to demonstrate the proposed model.

[1]  Yili Hong,et al.  Field-Failure Predictions Based on Failure-Time Data With Dynamic Covariate Information , 2013, Technometrics.

[2]  Zhengqiang Pan,et al.  Reliability modeling of degradation of products with multiple performance characteristics based on gamma processes , 2011, Reliab. Eng. Syst. Saf..

[3]  C. Radhakrishna Rao,et al.  Prediction of Future Observations in Growth Curve Models , 1987 .

[4]  Xiao Wang,et al.  Wiener processes with random effects for degradation data , 2010, J. Multivar. Anal..

[5]  Yili Hong,et al.  Reliability Analysis of Repairable Systems With Dependent Component Failures Under Partially Perfect Repair , 2013, IEEE Transactions on Reliability.

[6]  Nick J. McCormick,et al.  Digital Image Correlation , 2010 .

[7]  G. C. Sih,et al.  Crack initiation behavior in magnetoelectroelastic composite under in-plane deformation , 2003 .

[8]  Yili Hong,et al.  Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data , 2015, Technometrics.

[9]  M. Simon Probability distributions involving Gaussian random variables : a handbook for engineers and scientists , 2002 .

[10]  Christophe Pinna,et al.  Local plastic strain evolution in a high strength dual-phase steel , 2010 .

[11]  Yili Hong,et al.  Reliability Meets Big Data: Opportunities and Challenges , 2014 .

[12]  M. Crowder,et al.  Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure , 2004, Lifetime data analysis.

[13]  O. Kolednik,et al.  The ductility of metal matrix composites – Relation to local deformation behavior and damage evolution , 2008 .

[14]  W. J. Padgett,et al.  Accelerated Degradation Models for Failure Based on Geometric Brownian Motion and Gamma Processes , 2005, Lifetime data analysis.

[15]  Hideki Hata,et al.  Local deformation and buckling of a cylindrical Al tube under magnetic impulsive pressure , 1999 .

[16]  Chun-Hway Hsueh Modeling of Elastic Deformation of Multilayers Due to Residual Stresses and External Bending , 2002 .

[17]  Fu-Kwun Wang,et al.  Lifetime predictions of LED-based light bars by accelerated degradation test , 2012, Microelectron. Reliab..

[18]  Yili Hong,et al.  Prediction of remaining life of power transformers based on left truncated and right censored lifetime data , 2009, 0908.2901.

[19]  M. Kuna,et al.  Determination of Ductile Damage Parameters by Local Deformation Fields: Measurement and Simulation , 2006 .

[20]  Standard Test Methods for Tension Testing of Metallic Materials 1 , 2022 .

[21]  [Prediction of Future Observations in Growth Curve Models]: Comment , 1987 .

[22]  Sun-Yeong Heo,et al.  Distribution of a Sum of Weighted Noncentral Chi-Square Variables , 2006 .

[23]  Wujun Si,et al.  A Generalized Mixed Effect Kijima Model and Application in Optimal Maintenance Planning , 2016, IEEE Transactions on Reliability.

[24]  Muhammad Ashraful Alam,et al.  A comprehensive model of PMOS NBTI degradation , 2005, Microelectron. Reliab..

[25]  Qingyu Yang,et al.  Optimal maintenance planning for repairable multi-component systems subject to dependent competing risks , 2015 .

[26]  Hyoung-Seop Kim,et al.  Finite element analysis of deformation behaviour of metals during equal channel multi-angular pressing , 2002 .

[27]  Wujun Si,et al.  Two-state optimal maintenance planning of repairable systems with covariate effects , 2018, Comput. Oper. Res..

[28]  Steven M. Cox,et al.  Stochastic models for degradation-based reliability , 2005 .

[29]  Rui Kang,et al.  Multivariate Degradation Modeling of Smart Electricity Meter with Multiple Performance Characteristics via Vine Copulas , 2017, Qual. Reliab. Eng. Int..

[30]  Jagnow Robert Carl,et al.  Real-time simulation of deformation and fracture of stiff materials , 2001 .

[31]  O. Kolednik The Characterization of Local Deformation and Fracture Properties – a Tool for Advanced Materials Design , 2006 .

[32]  Xin Wu,et al.  A physical–statistical model of overload retardation for crack propagation and application in reliability estimation , 2016 .

[33]  Nailong Zhang,et al.  A random effect autologistic regression model with application to the characterization of multiple microstructure samples , 2016 .

[34]  A. Castaño-Martínez,et al.  Distribution of a sum of weighted noncentral chi-square variables , 2005 .

[35]  Wujun Si,et al.  Accelerated Life Testing With Semiparametric Modeling of Stress Effects , 2017, IEEE Transactions on Reliability.

[36]  Xin Wu,et al.  A distribution-based functional linear model for reliability analysis of advanced high-strength dual-phase steels by utilizing material microstructure images , 2017 .

[37]  A. Shabbir,et al.  Multiple Cracks Propagate Simultaneously in Polymer Liquids in Tension. , 2016, Physical review letters.

[38]  G. Seber,et al.  Nonlinear Regression: Seber/Nonlinear Regression , 2005 .

[39]  C. Joseph Lu,et al.  Using Degradation Measures to Estimate a Time-to-Failure Distribution , 1993 .

[40]  A. M. Mathai,et al.  Quadratic forms in random variables : theory and applications , 1992 .

[41]  E. Elsayed,et al.  A general accelerated life model for step-stress testing , 2005 .

[42]  Ran Jin,et al.  Nonlinear general path models for degradation data with dynamic covariates , 2016 .

[43]  Bin Wang,et al.  Nonlinear tensile deformation behavior of small-sized metallic glasses , 2009 .

[44]  Heinz Werner Höppel,et al.  Fatigue and microstructure of ultrafine-grained metals produced by severe plastic deformation , 2004 .

[45]  John D. Clayton,et al.  Deformation, fracture, and fragmentation in brittle geologic solids , 2010 .

[46]  Nobuo Takeda,et al.  Effects of Moisture Content on Nonlinear Deformation Behavior of CF/Epoxy Composites , 1997 .