On the tribological and thermal aspects of cryogenic machining of Inconel 718 and their effects on surface integrity
Bibliographic record
Abstract
Current trends aim at improving the surface integrity of aerospace Ni-based superalloys by reducing the temperature rise in the cutting zone in order to minimize tool wear, as well as reducing the thermally induced tensile residual stresses on the machined surface. The manufacturing industry is also driven by developing high performance sustainable machining processes. Cryogenic machining (CM) using Liquid Nitrogen (LN2) has recently gained interest and momentum as a clean and economical cooling technique, especially for applications involving aggressive metal removal of hard-to-cut materials. This research aims at understanding the physical, thermal and tribological aspects of LN2 cryogenic machining and optimizing the machining process based on computational fluid dynamics CFD analysis results. Experimental investigation of the turning operation of Inconel 718 (IN718) were carried out at different LN2 delivery configurations and compared to high pressure coolant (HPC) and flood cooling. The effect of the cooling strategy on the machining performance was evaluated in terms of tool wear, friction at the tool-chip interface and surface integrity of machined parts. Progressive tool wear tests showed a 20 % reduction in tool wear compared to high pressure cooling. This can be attributed to the effective penetration of LN2 to the cutting area (tool-chip contact interface, and the cutting edge), which leads to improve the cooling and lubrication capacity of the LN2 jet. Residual stresses measurement in the subsurface layer of machined parts showed that a tensile residual stress of 50 MPa at the surface was obtained for flood cooling, due to the high cutting temperature. On the other hand, cryogenic cooling produced compressive residual stresses of up to 600 MPa at the surface, which are very beneficial for the part quality and fatigue life.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".