Hard-to-Change Flux Factor on the Wave Solder Line
At a printed-circuit-board plant the wave-solder first-pass yield is 91.2% against a 96% target; the dominant defect is bridging, at 62% of defects. A team ran a designed experiment on three factors: preheat temperature (100 vs 120 C, easy to change), conveyor speed (0.8 vs 1.0 m/min, easy), and flux type (A vs B). Changing flux requires draining and cleaning the reservoir, about 4 hours, so the 8-run full factorial plus 3 center points was run by setting flux type once each morning, then randomizing the four preheat/speed combinations within the day, with 2 replicates. Analysis shows speed (p = 0.002), flux (p = 0.001), and the flux x speed interaction (p = 0.01) significant; preheat is not. The best predicted setting is flux B at 0.7 m/min, but 0.7 m/min was never tested. Bridging is currently 41 per 1,000 joints; the model predicts 12 per 1,000 at the predicted optimum.
1. Identify the design and analysis problem created by the hard-to-change flux factor and state how the experiment should have been structured and analyzed.
Read the worked answer
The structural problem The experiment was executed as a split-plot but analyzed as if it were completely randomized. Flux was held constant for a whole day, so the four preheat/speed combinations at one flux level all share that day's conditions - operator, ambient temperature, solder pot chemistry, board lot. Those day effects are perfectly confounded with flux, and the sub-plot runs within a day are not independent of each other.
Consequences of analyzing it as a CRD
- The flux effect is tested against the sub-plot (residual) error, which is far too small for a whole-plot factor. Its F-value is inflated, so p = 0.001 is optimistic and may not survive correct analysis.
- The flux x speed interaction involves a whole-plot factor, so its standard error is also understated.
- Any day-to-day drift is silently attributed to flux.
How it should have been structured
- Declare flux as a hard-to-change (whole-plot) factor, randomize the order of flux levels across days, and replicate whole plots - at least 2-3 separate days per flux level, so the flux comparison is not a single-day comparison.
- Analyze with a mixed model containing two error strata: whole-plot error for flux, sub-plot error for speed, preheat, and the interactions. Equivalently, a split-plot ANOVA with the correct denominators.
- If flux truly cannot be replicated across days, treat it as a block and accept that its main effect is not estimable - do not report it as significant.
Message for the team: randomization must match the physical constraints. When a factor cannot be randomized run-to-run, the analysis must say so.
2. The recommended optimum is flux B at 0.7 m/min. What is your judgment on that recommendation, and what runs would you do next?
Read the worked answer
Judgment: reject it as stated
- 0.7 m/min is outside the tested region (0.8-1.0). The fitted model is linear in speed, but there is no data to support the shape below 0.8, and the significant flux x speed interaction means the speed slope itself depends on flux. Extrapolating a linear model past the design space is one of the classic DOE failure modes.
- The predicted 12 per 1,000 is a model extrapolation, not an observation. It should be treated as a hypothesis to be tested, not a result to be implemented.
- A lower conveyor speed changes other physics: more preheat dwell, different thermal profile, possible board warp, reduced throughput, and possibly new defect modes (dross, icicles, cold joints) that this experiment did not measure.
- Economics matter. A 4-hour flux changeover means flux B should be run in campaigns. If the throughput loss from 0.8 to 0.7 m/min costs more than the bridges saved, the economic optimum is likely flux B at 0.9 or 1.0, not 0.7.
Next runs
- Confirmation runs at the two credible settings: flux B at 0.8 and flux B at 1.0, replicated on separate days.
- Augment the design with speed 0.7 and 0.9 (and optionally 1.1) at flux B, with fresh center points to test for curvature and lack of fit.
- Measure the full response set, not just bridging: total defects by type, joint strength or cross-section, ionic contamination, throughput in boards per hour, and rework hours.
- Compare cost per 1,000 joints, including changeover time, before choosing.
Only after confirmation runs land inside the acceptance window should the setting be proposed for a pilot.
3. The team wants to skip the pilot and go straight to plant-wide implementation. What would you require before scale-up?
Read the worked answer
Pre-defined acceptance criteria (agreed before the pilot starts)
- Bridging at or below 12 per 1,000 joints, with the 95% upper confidence bound also at or below the level implied by 96% yield - a point estimate alone is not enough.
- Total first-pass yield >= 96%, and no increase in any other defect category (cold joints, icicles, dross, contamination).
- Joint quality passes visual and cross-section acceptance; flux residue passes ionic contamination and compatibility checks with conformal coating.
- Throughput at or above the current baseline; if it drops, the business case must still hold on total cost.
- Process parameters stay inside the machine's capability window (preheat, pot temperature, wave height) across a full shift.
Pilot design
- One line, two to three production lots per setting, randomized run order, same operators and boards, defect counting blinded or verified by attribute agreement.
- Run the current setting and the proposed setting side by side rather than sequentially by week, to avoid confounding with lot or season.
- Written stop rules: any safety issue, any out-of-spec joint quality, or two consecutive lots above the bridging threshold ends the trial.
Before scale-up, also
- Update the PFMEA with the new failure modes and re-score; mistake-proof flux selection with a recipe interlock so the wrong flux cannot be loaded.
- Update the control plan with the new settings, monitoring method, and reaction plan; train operators on the changeover procedure.
- Verify flux B supply, shelf life, cost, and lead time - a yield gain that creates a single-source dependency is not a clean win.
- Scale line by line with a confirmation lot at each line, not all at once.