Supporting Data and Analysis Code for Repeated Soldering of SAC305-Bonded Mild Steel Lap Joints
Description
This dataset contains the experimental mechanical-testing data and Python analysis code supporting the study of repeated soldering and unsoldering of SAC305-bonded mild steel lap joints for repairable electric vehicle battery interconnects. It includes raw force-displacement-stress records, master maximum-load results, processed specimen-level datasets, shear-strength summaries, and statistical-test outputs for mild steel-SAC305-mild steel joints tested after 1, 2, 4, 5, and 8 solder-unsolder cycles. A sparse mild steel-SAC305-copper control dataset is also included as a maximum-load benchmark. The accompanying Python scripts reproduce the data processing, bonded-area normalisation, summary statistics, and numerical plots. Peak shear strength is calculated by normalising maximum load using the measured bonded area. No specimens were removed as outliers. The dataset does not include the manuscript, submitted publication figures, or SEM/workflow image assets.
Files
Steps to reproduce
1. Download and extract the dataset archive. 2. Install Python 3.10 or newer. 3. Open a terminal in the extracted dataset folder. 4. Install the required packages: ```powershell python -m pip install -r requirements.txt ``` 5. For final publication-style figure rendering, install TeX Live or MiKTeX and ensure `latex` is available on the system path. 6. Run the complete workflow: ```powershell python scripts\run_all.py ``` 7. The scripts regenerate the processed CSV data in `data/processed/` and numerical figures in `figures/generated/`. 8. If LaTeX is unavailable, create draft figures using: ```powershell python scripts\run_all.py --draft ``` The workflow uses the raw Excel workbooks in `data/raw/`. Peak shear stress is obtained from each corrected stress trace; maximum load is read from the master results workbook; bonded area is calculated as `1000 × Fmax (kN) / τmax (MPa)`.
Institutions
- University of SheffieldEngland, Sheffield