Bibliographic record
Abstract
Purpose WC-Co is a well-known material for conventional tooling but is not yet commercially available for additive manufacturing. Processing it by selective laser sintering (SLS) will pave the way for its commercialization and adoption. Design/methodology/approach It is intended to optimize process parameters (laser power, hatch spacing, scan speed) by fabricating a bigger part (minimum size of 10 mm diameter and 5 mm height). Microstructural analysis, EDX and hardness testing is used to study effects of process parameters. Optimized parameter is ascertained after fabricating 49 samples in preliminary experiment, 27 samples in pre-final experiment and 9 samples in final experiment. Findings Higher laser power gives rise to cracks and depletion of cobalt while higher scan speed increases porosity. Higher hatch spacing is responsible for delamination and displacement of parts. Optimized parameters are 270 W laser power, 500 mm/s scan speed, 0.04 mm layer thickness, 0.04 mm hatch spacing (resulting in energy density of 216 J/mm3) and 200°C powder bed temperature. A part comprising of small hole of 2 mm diameter, thin cylindrical pin of 0.5 mm diameter and thin wall of 2 mm width bent up to 30° angle to the base plate is fabricated. In order to calculate laser energy density, a new equation is introduced which takes into account both beam diameter and hatch spacing unlike old equation does. In order to calculate laser energy density, a new equation is formulated which takes into account both beam diameter and hatch spacing unlike old equation does. WC was not completely melted as intended giving rise to partial melting-type binding mechanism. This justified the name SLS for process in place of SLM (Selective Laser Melting). Research limitations/implications Using all possible combination of parameters plus heating the part bed to maximum shows limitation of state-of-the-art commercial powder bed fusion machine for shaping hardmetal consisting of high amount of WC (83 wt. per cent). Practical implications The research shows that microfeatures could be fabricated using WC-Co which will herald renewed interest in investigating hardmetals using SLS for manufacturing complex hard tools, molds and wear-resistance parts. Originality/value This is the first time micro features are successfully fabricated using WC-Co without post-processing (infiltration, machining) and without the help of additional binding material (such as Cu, Ni, Fe).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".