An improved machining temperature prediction model for aerospace alloys: Effect of cutting edge radius and tool wear
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Temperature rise during machining impacts the workpiece material properties, residual stresses, surface and sub-surface quality. Experimental, numerical, and analytical methods have been used to predict the temperature fields in the tool, workpiece and chip. Each approach has its limitations: experimental techniques are cumbersome with expensive equipment, and numerical modeling is computationally inefficient. Existing analytical models only consider the effect of wear while ignoring the edge radius , though the latter changes with the flank wear in practice. To address this limitation, this article proposes an improved analytical temperature prediction model for orthogonal machining by introducing discrete linear heat sources on the edge radius of the cutting edge. The model describes the machining deformation zones by moving or stationary heat sources and models the adiabatic surfaces by imaginary heat sources. The heat partition is calculated to describe the amount of temperature transferred from a heat source to a given body. A global coordinate system is introduced to facilitate the integration of the edge radius in the temperature model, and variation in the direction of the heat source velocity. Temperature predictions of the developed model were experimentally verified using an inverse method based on XRD residual stress measurements . The results of the analysis show that the proposed model is reasonably accurate and most importantly computationally efficient alternative to tedious experimental measurements or more complicated finite element approaches.
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.
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 | 0.001 |
| 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 it