Taguchi-based experimental investigation into weld cladding of Ni-WC MMC overlays by CMT process
Bibliographic record
Abstract
In the search for versatile and effective weld cladding processes to deposit ultra-wear-resistant Ni-WC MMC (Ni-based tungsten carbide metal matrix composite) overlays for mining applications, there is an increasing interest in exploring advanced low-heat-input cold metal transfer (CMT) method. Depositions of single weld bead tracks of Ni-WC MMCs on steel plates were performed by employing the CMT process; Taguchi's design of experiments was used to plan the experimental investigation. All weld tracks exhibit continuous and uniform bead profile and sound metallurgical bonding to the substrate. Retained WCs are present in the overlay tracks relatively uniformly. The formation of primary WC and secondary carbides is observed depending on the level of dilution. In contrast to standard gas metal arc welding processes, the volume fraction of retained WC, which is negatively correlated with dilution level, is not directly interrelated with heat input for the CMT process and can reach a high level together with improved weld bead appearance at high deposition rate. Deposition rate has a positive correlation with average instantaneous power, which is, in turn, positively correlated with wire feed speed. The addition of oxygen into shielding gas mixtures promotes carbide transfer from cored feed wire to the weld track and increases the volume fraction of retained WC. Analysis of signal-to-noise ratios shows that it is difficult to find a single set of optimized processing parameters, and trade-offs are needed in engineering practice. The present investigation demonstrates that the Taguchi method is a powerful tool in process improvement for weld cladding of Ni-WC MMC overlays.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".