Physics based Prognostics of Solder Joints in Avionics
Bibliographic record
Abstract
Applicability of a physics based prognostics approach for solder joints using microstructural damage models is investigated. A modified deformation mechanism map for the solder alloys is introduced where grain boundary sliding (GBS) plays a dominant role during creep deformation. The high homologous temperature of solder as well as the combined thermal-vibration cycling experienced during typical operating missions necessitates the use of a combined creep-fatigue failure approach. In this work, a PCB consisting of a heat generating chip with Ball-Grid Array (BGA) solder joints is considered for avionics application. A prognostics based Life Cycle Management approach was used to perform the mission analysis, FEA, thermal-mechanical stress analysis and damage accumulation analysis. The remaining useful life (RUL) is predicted for different rupture strains. The uniqueness of this approach lies in the use of microstructure based damage models and consideration of both material and mission variability to predict the RUL under actual usage. The life critical nodes were observed near the junction of the solder joints with the substrate due to high disparities in their coefficients of thermal expansion. In addition, the probabilistic analysis was also performed by randomly varying the grain size and fitting a two-parameter Weibull distribution to the failure data. The model calibration and the results show some practical trends that need to be verified through future experimentation. The simulation results demonstrate the viability of using a physics-based approach for the prognosis of solder joint failures in avionics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".