| 1. |
- Xu, Fei, et al.
(author)
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Fatigue Life Prediction for PBGA under Random Vibration Using Updated Finite Element Models
- 2016
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In: Experimental techniques (Westport, Conn.). - : Springer. - 0732-8818 .- 1747-1567. ; 40:5, s. 1421-1435
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Journal article (peer-reviewed)abstract
- A procedure based on finite element (FE) modeling, response surface-based model updating and random vibration analysis is presented to predict the fatigue life of plastic ball grid array (PBGA) components mounted on daisy chain printed circuit board (PCB). A specially designed fixture is used to mimic the typical boundary condition of plug-in PCBs. The FE model is updated through three consecutive stages. In each stage, the first three resonant frequencies are calculated using ANSYS and correlated with modal test results. Two objective functions are created using resonant frequencies and minimized using a multi-objective genetic algorithm (MOGA). The results show that commercial FE software can be used to improve the accuracy of the FE model in a practical way. Random vibration analysis is performed and good agreement with test result is obtained. The resistance of the specially designed daisy chain PCB is monitored. A two-parameter Weibull distribution is used to fit the PBGA failure time. The Von Mises stress power spectral density (PSD) of the critical solder joints is calculated in ANSYS and transferred into time-history data. The rainflow cycle counting (RFCC), the S–N curve and the Miner’s rule are used to estimate the cumulative damage. The calculated fatigue life agrees well with the test results.
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| 2. |
- Xu, Fei, et al.
(author)
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On the Shaker Simulation of Wind-Induced Non-Gaussian Random Vibration
- 2016
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In: Shock and Vibration. - : Hindawi Publishing Corporation. - 1070-9622 .- 1875-9203.
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Journal article (peer-reviewed)abstract
- Gaussian signal is produced by ordinary random vibration controllers to test the products in the laboratory, while the field data is usually non-Gaussian. Two methodologies are presented in this paper for shaker simulation of wind-induced non-Gaussian vibration. The first methodology synthesizes the non-Gaussian signal offline and replicates it on the shaker in the Time Waveform Replication (TWR) mode. A new synthesis method is used to model the non-Gaussian signal as a Gaussian signal multiplied by an amplitude modulation function (AMF). A case study is presented to show that the synthesized non-Gaussian signal has the same power spectral density (PSD), probability density function (PDF), and loading cycle distribution (LCD) as the field data. The second methodology derives a damage equivalent Gaussian signal from the non-Gaussian signal based on the fatigue damage spectrum (FDS) and the extreme response spectrum (ERS) and reproduces it on the shaker in the closed-loop frequency domain control mode. The PSD level and the duration time of the derived Gaussian signal can be manipulated for accelerated testing purpose. A case study is presented to show that the derived PSD matches the damage potential of the non-Gaussian environment for both fatigue and peak response.
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| 3. |
- Xu, Fei, et al.
(author)
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Synthesis of running RMS-induced non-Gaussian random vibration based on Weibull distribution
- 2015
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In: Journal of Vibroengineering. - 1392-8716. ; 17:7, s. 3662-3674
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Journal article (peer-reviewed)abstract
- Gaussian signal is produced by ordinary random vibration controllers to test the products in the laboratory, while the field data usually is non-Gaussian. To synthesize non-Gaussian random vibration, both the probability density function (PDF) and the damage effects must be considered. A new method is presented in this paper to synthesize non-Gaussian random vibration that is characterized by running RMS (root mean square). The essential idea is to model the non-Gaussian signal by a Gaussian signal multiplied by an amplitude modulation function (AMF). A two-parameter Weibull distribution is used to model the PDF of the running RMS and to create the AMF. The shock response spectrum (SRS) is used to detect significant shocks within the non-Gaussian signal. A case study is presented to show that the synthesized non-Gaussian signal has the same power spectral density (PSD), kurtosis, PDF and fatigue damage spectrum (FDS) as the field data.
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