Improvement of the Wear Resistance of Circular Saws Used in the First Transformation of Wood through the Utilization of Variable Engineered Micro-Geometry Performed on PVD-Coated WC-Co Tips
Bibliographic record
Abstract
Reduced performance of circular saws due to premature chipping of their teeth has been a critical issue in woodcutting industry for many years. This research examined the impact of surface coating and variable engineered micro-geometry of the cutting edges of carbide teeth (tips) on the wear resistance of circular saws used in primary wood processing. CrN/CrCN/DLC, CrN/AlTiN, CrN/CrCN, and CrCN/TiSiCN were deposited on tungsten carbide-cobalt (WC-Co) substrates using the cathodic arc evaporation technique. The CrN/CrCN coating proved to be the one with highest wear resistance and adhesion among those studied. No sign of delamination was observed around the indentation of the CrN/CrCN coating after the adhesion test. Furthermore, no abrasion, delamination or crack was observed on the surface of the CrN/CrCN coating after the three-body abrasion wear test. The results of the dry-sliding wear test revealed that CrN/CrCN coating significantly decreased the wear rate of WC-Co substrates by 74%, 66% and 77% at sliding speeds of 50, 100 and 250 mm/s, respectively. Afterwards, a CrC/CrCN coating was deposited on the teeth of conventional circular saws. Next, the cutting edges of teeth were modified through variable engineered micro-geometry. Tests were conducted at a sawmill with three series of saws: 1-coated and edge-modified, 2-coated and conventional edge geometry, and 3-uncoated and edge-modified. Wood processing was performed during two shifts of 480 min each. The width of the wear land was the criterion used as the wear index. The results of industrial tests showed that saws with edge-modified teeth had significantly less chipping and no breakage at their corners compared to the saw without edge modification (conventional saw). After 480 min of sawing, the wear rate of the coated saw with edge modification decreased by 46% and 16%, compared to the coated saw without edge modification and the uncoated saw with edge-modified teeth, respectively. Those values reached 73 % and 41%, respectively, after 960 min of sawing. The study shows that by optimizing the surface chemistry and the geometry of the cutting edge of WC-Co tips, tool life can be significantly increased therefore reducing downtime due to saw replacement and resharpening, thus significantly increasing productivity in the first transformation of wood.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".