Using Predictive Modeling and Classification Methods for Single and Overlapping Bead Laser Cladding to Understand Bead Geometry to Process Parameter Relationships
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
Developing a bead shape to process parameter model is challenging due to the multiparameter, nonlinear, and dynamic nature of the laser cladding (LC) environment. This introduces unique predictive modeling challenges for both single bead and overlapping bead configurations. It is essential to develop predictive models for both as the boundary conditions for overlapping beads are different from a single bead configuration. A single bead model provides insight with respect to the process characteristics. An overlapping model is relevant for process planning and travel path generation for surface cladding operations. Complementing the modeling challenges is the development of a framework and methodologies to minimize experimental data collection while maximizing the goodness of fit for the predictive models for additional experimentation and modeling. To facilitate this, it is important to understand the key process parameters, the predictive model methodologies, and data structures. Two modeling methods are employed to develop predictive models: analysis of variance (ANOVA), and a generalized reduced gradient (GRG) approach. To assist with process parameter solutions and to provide an initial value for nonlinear model seeding, data clustering is performed to identify characteristic bead shape families. This research illustrates good predictive models can be generated using multiple approaches.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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