Determination and interpretation of activation energy using accelerated-test data

2002 ◽  
Author(s):  
D.J. Groebel ◽  
A. Mettas ◽  
F.-B. Sun
Keyword(s):  
Activation Energy ◽  
Test Data ◽  
Accelerated Test

A Processing Method for Combined Fatigue Accelerated Test Data

2017 ◽  
Author(s):  
Songwang Zheng ◽  
Cao Chen ◽  
Lei Han ◽  
Xiaoyong Zhang ◽  
Xiaojun Yan
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Turbine Blades ◽  
Processing Method ◽  
Test Time ◽  
Accelerated Test ◽  
The Real ◽  
Data Processing Method ◽  
Stress Levels ◽  
Vibration Stress

To carry out combined low and high cycle fatigue (CCF) test on turbine blades in a bench environment, it is imperative to simulate the vibration loads of turbine blades in the field. Due to the low vibration stress of turbine blades in the working state, the test time will be very long if the test vibration stress is equal to the real vibration stress in working state. Therefore, an accelerated test will be used when the test life reach the target value (typically 107). During the accelerated test, each blade is tested at two or more times than the real vibration stress. That means some specimens are tested under two vibration stress levels. In this case, a reasonable data processing method becomes very important. For this reason, a data processing method for the CCF accelerated test is proposed in this paper. These test data are iterated on the basis of S-N curve. Finally, ten real turbine blades are tested in a bench environment, one of them is tested under two vibration stress levels. The test data is processed using the method proposed above to obtain the unaccelerated life data.

PCR for Derivation of Parameter Dependencies, Thermo-Mechanical Norris-Landzberg Acceleration Factors, Goldmann Fatigue Constants for Leadfree Electronics

2009 ◽  
Author(s):  
Pradeep Lall ◽  
Aniket Shirgaokar ◽  
Dineshkumar Arunachalam ◽  
Jeff Suhling ◽  
Mark Strickland ◽  
...  
Keyword(s):  
Test Data ◽  
Thermal Cycle ◽  
Regression Models ◽  
Reliability Prediction ◽  
Maximum Temperature ◽  
Temperature Cycle ◽  
Data Sets ◽  
Accelerated Test ◽  
Bga Packages ◽  
Part Selection

Goldmann Constants and Norris-Landzberg acceleration factors for lead-free solders have been developed based on principal component regression models (PCR) for reliability prediction and part selection of area-array packaging architectures under thermo-mechanical loads. Models have been developed in conjunction with Stepwise Regression Methods for identification of the main effects. Package architectures studied include, BGA packages mounted on copper-core and no-core printed circuit assemblies in harsh environments. The models have been developed based on thermo-mechanical reliability data acquired on copper-core and no-core assemblies in four different thermal cycling conditions. Packages with Sn3Ag0.5Cu solder alloy interconnects have been examined. The models have been developed based on perturbation of accelerated test thermo-mechanical failure data. Data has been gathered on nine different thermal cycle conditions with SAC305 alloys. The thermal cycle conditions differ in temperature range, dwell times, maximum temperature and minimum temperature to enable development of constants needed for the life prediction and assessment of acceleration factors. Goldmann Constants and the Norris-Landzberg acceleration factors have been benchmarked against previously published values. In addition, model predictions have been validated against validation data-sets which have not been used for model development. Convergence of statistical models with experimental data has been demonstrated using a single factor design of experiment study for individual factors including temperature cycle magnitude, relative coefficient of thermal expansion, and diagonal length of the chip. The predicted and measured acceleration factors have also been computed and correlated. Good correlations have been achieved for parameters examined. Previously, the feasibility of using multiple linear regression models for reliability prediction has been demonstrated for flex-substrate BGA packages [Lall 2004, 2005], flip-chip packages [Lall 2005] and ceramic BGA packages [Lall 2007]. The presented methodology is valuable in the development of fatigue damage constants for the application specific accelerated test data-sets and provides a method to develop institutional learning based on prior accelerated test data.

Error Correction of Theory Model in Process-Stress Accelerated Test

2011 ◽  
Vol 311-313 ◽  
pp. 1677-1680
Author(s):  
Chun Sheng Guo ◽  
Qian Qian Du ◽  
Shi Wei Feng
Keyword(s):  
Activation Energy ◽  
Theoretical Model ◽  
Theoretical Data ◽  
Theory Model ◽  
Test Method ◽  
Accelerated Test ◽  
New Model ◽  
Computer Aided ◽  
Model Algorithm ◽  
Process Stress

To correct error in theoretical model of process-stress accelerated test, a new calculation method is proposed. The new method, based on computer-aided calculation, can significantly reduce the error of the model. Theoretical data is calculated using both the new model algorithm, which is the root test method, and the old model algorithm. The results show that the old model algorithm can generate error more than 13% in the activation energy and error more than 150% in the extrapolated lifetime (Q≤1.0eV), while the new model algorithm generates error less than 1% in activation energy, and error less than 4.1% in the extrapolated lifetime.

scholarly journals Photodegradation modeling based on laboratory accelerated test data and predictions under outdoor weathering for polymeric materials

2017 ◽  
Vol 11 (4) ◽  
pp. 2052-2079 ◽  
Author(s):  
Yuanyuan Duan ◽  
Yili Hong ◽  
William Q. Meeker ◽  
Deborah L. Stanley ◽  
Xiaohong Gu
Keyword(s):  
Test Data ◽  
Polymeric Materials ◽  
Accelerated Test ◽  
Outdoor Weathering

Lifetime prediction using accelerated test data and neural networks

2009 ◽  
Vol 87 (19-20) ◽  
pp. 1187-1194 ◽  
Author(s):  
S. Freitag ◽  
M. Beer ◽  
W. Graf ◽  
M. Kaliske
Keyword(s):  
Neural Networks ◽  
Test Data ◽  
Lifetime Prediction ◽  
Accelerated Test
Sign in / Sign up

Export Citation Format

Share Document