Identifying Relative Importance of Input Parameter(s) for Developing Predictive Model for Laser Cladding Process
Bibliographic record
Abstract
Laser cladding (LC) is a multi-variable coating process which consists of process multiple inputs and associated bead geometry outputs. Fabrication of a desired clad bead geometry configuration is expensive, as it involves investment of specialized raw materials, specialty equipment, and time resources. Hence, it is vital to determine factors/inputs that affect the overall physical bead geometry parameters (response variables), and the nature of the responses. The objective of this research is to identify the extent of the contribution of each factor and impact of their interactions on the output which is essential in developing effective predictive models. Analysis of variance (ANOVA) and sensitivity analysis methodologies are studied in this research to determine the most significant process factors that relate to the shape parameters for a typical laser cladding production process scenario. A set of statistical based summaries for all response variables are presented. This includes contour and surface plots to illustrate the difference in effects for a response variable by a single process parameter as compared to two or more interacting process parameters. Finally, an optimization solver toolbox is applied to determine single and multiple objective optimization results that can be obtained for various desired bead geometries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".