Effect of Dimensional Variation on Induction Process Parameters Using 2D Simulation
Bibliographic record
Abstract
The induction heating is a surface heat treatment that exhibits some relevant industrial advantages. In fact, the process is not energy-consuming compared to thermo-chemical processes such as carbonizing and nitriding because it allows generating high power and focusing it locally and during a short time to achieve hardness at the surface area without affecting the part core. Using no plating phase, the induction heating process is qualified as green and sustainable manufacturing process but should be better understood to help developers to reach optimized recipes in a small number of process iterations. Globally, for a given range of parts to be manufactured, one has to proper select the frequency and power of the equipment to be. This work will show how part geometry, generator frequency and power are closely linked. This work is carried principally by simulation efforts using computer-modeling software (COMSOL). A developed 2D model includes the coupling between electro-magnetic and thermal fields, and takes account of the non-linear behavior of material properties versus temperature. The simulation allows optimizing the machine according to the dimensions of gear. This paper also proposes a method to approximate the power amount required to achieve a desired hardness profile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".