Bibliographic record
Abstract
ABSTRACT On-demand webinar originally hosted by SMTA Ontario Chapter Technical Presentation: Design for cleaning is a laudable goal; it is often unachievable. Nearly all high-value electronics assemblies require post-reflow cleaning to remove flux and the residue of an assortment of thin film and particulate manufacturing residue. We will explain why “no-clean flux” must be removed and how soil and soil residue differ. Topics include increased assembly complexity, miniaturization, increased product performance requirements, and the impact of safety/environmental regulations. To meet upcoming challenges, electronics assemblers must get strategic about cleaning. We will outline the range of cleaning choices and explain the inherent physical and chemical limitations of water. We will explain the importance of the collaboration between components suppliers and final assemblers. Cleaning techniques that expand the horizons of soil removal include high frequency ultrasonics, cyclic cavitation (cyclic nucleation), and non-chemical cleaning options such as CO2, steam, and laser cleaning. We will discuss the potential and limitations of newer techniques as well as testing requirements. Speaker Bio: Barbara Kanegsberg , “the Cleaning Lady” of BFK Solutions LLC, has experience and expertise in cleaning agent and cleaning process selection, analytical chemistry, and troubleshooting manufacturing processes. Ed Kanegsberg, “the Rocket Scientist” of BFK Solutions, is a chemical physicist and engineer who troubleshoots and solves manufacturing production problems. As independent experts and advisers, they help manufacturers achieve rugged, trouble-free processes in areas such as electronics, metal forming and fabrication, aerospace, medical device manufacturing, optics, and consumer products. They are co-editor/contributors for the classic 2 Volume Second Edition of the “Handbook for Critical Cleaning,” CRC Press, 2011. They conduct dynamic, interactive workshops and training programs, including real time and on-demand Product Quality Cleaning Workshops, with Sam Houston State University. Files Available to Download: Recorded Presentation (On-Demand) Slides (PDF)
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.070 |
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".