Reliability Improvement of Lithium Cells Using Laser Welding Process with Design of Experiments
Bibliographic record
Abstract
Manufacturing operations introduce unreliability into hardware that is not ordinarily accounted for by reliability design engineering efforts. Inspections and test procedures normally interwoven into fabrication processes are imperfect, and allow defects to escape which later result in field failures. Therefore, if the reliability that is designed and developed into an equipment/system is to be achieved, efforts must be applied during production to insure that reliability is built into the hardware. There are various ways to improve the reliability of a product. These include: Simplification Stress reduction/strength enhancement Design Improvement Using higher quality components Environmental Stress Screening before shipment Process Improvements, etc. This paper concentrates on ‘Manufacturing Process Improvement’ effort through the use of design of experiments, (DOE). Hence, improved levels of reliability can be achieved. Most applications optimize the mean value of a response variable. Parameter design experiments are aimed at reducing variability caused by manufacturing variations. In industrial processes, controlling variability is usually much harder than controlling the mean value. And variability is often a root cause of manufacturing problems. Optimizing the process of welding the lithium cells hermetically, by laser, have been focused in this work. Cell cans were approximately 1.5 inch diameter, nickel-plated, cold rolled steel with the top welded on around a 0.040 inch lip. The important characteristic measured in this work is weld penetration depth. The key steps in this project are: 1 Identify important process parameters / factors, (controllable and un-controllable / noise), and their potential working levels that can be manipulated. 2 Design and perform ‘Fractional Factorial Experiments’ on the process. 3 Analyze the resulting data to determine the optimum operating levels of the factors such that the product's functional characteristic is optimized and has minimal sensitivity to noise.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".