Selective Soldering with Sn3.9Ag0.6Cu: Process Development
Bibliographic record
Abstract
ABSTRACT Selective soldering is a process for soldering thru-hole components on the bottom side of an assembly. In a selective soldering process a robot system is used to pick up the assembly and drag it over a single point select wave, or dip the assembly into multiple nozzles that are mounted on a product specific nozzle plate. In contrast, the soldering process utilizing traditional wave soldering equipment consists of transporting the entire assembly, by means of a conveyor system, over a liquid solder wave. The implementation of lead-free alloys will affect the thermal load of components and board material due to higher process temperatures. Not all of the components can withstand the high temperatures that they may be exposed to during wave and reflow processes. For this reason selective soldering becomes interesting since in this process only the areas that come into contact with the lead-free solder will reach higher temperatures while other sections and components remain significantly below critical temperature levels. This paper focuses on the effects of board surface finishes and selective soldering process parameters such as drag speed and solderpot temperatures, on the formation and strength of the solder joints. The analysis shows that the process development and material selection should be optimized per component type. This work was accomplished within the scope of the NEMI Lead Free Assembly and Rework Project.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".