Benchmarking and QFD: accelerating the successful implementation of no clean soldering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 1989, with the signing of the Montreal Protocol, the process of cleaning printed circuit boards was challenged. Chlorofluoro-carbons or CFCs, which had long been used as cleaning agents in the industry, were no longer acceptable. During this same time period, consumers began demanding faster, smaller, and cheaper computers. To meet these needs, "no clean" processes were introduced. By eliminating cleaning, cost and cycle time are reduced and product reliability is increased. Austin's Electronic Card Assembly and Test (ECAT) facility proceeded on the journey from CFC cleaning to aqueous cleaning and then on to the implementation of no clean materials. "No clean" processes in printed circuit board manufacturing provide an excellent way to decrease cost and cycle time while improving the process and environment. However, conversion to these new process materials presents new challenges. To accelerate successful implementation, companies that had already converted to no clean were benchmarked and then quality functional deployment (QFD) techniques were used to prioritize needs and concerns. Benchmarking was used to determine and avoid pitfalls, save qualification costs, and reduce implementation time. QFD was used for translating the voice of the customer into product and/or process requirements. By coordinating skills within the organization to evaluate, then qualify the materials and processes, we were able to achieve customer satisfaction and greatly reduce the time taken in making similar changes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it