Real-Time Prediction of Temperature Distribution in Additive Manufacturing Processes Based on Machine Learning
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Simulation tools improve various aspects of the additive manufacturing process, however, they come with an undesirable computational time for real-world applications. Finite element analysis (FEA) that solves partial differential equations (PDE) presents promising capabilities in simple additive manufactured components as an expository problem. Yet, PDE-based solutions take significantly long CPU time due to a large number of timesteps required to simulate an additively manufactured part. With modern machine learning (ML) capabilities, a new shift towards integration of FEA and ML has been introduced, where ML algorithms emulate the behavior of the time-consuming PDE-solver for real-time analysis of PDE in a given application. In this paper, we present a deep learning (DL) model that can substitute the thermal analysis of the additive manufacturing process. The training data is obtained by sampling the established physical model’s behavior over different temperatures, cooling rates, and part’s geometries. The network architecture is composed of a Long Short-Term Memory (LSTM) to model the temporal sequence of deposition temperatures derived by PDEs. The reported R2 value on validations data is 97%, while the Mean Absolute Error (MAE) is 0.04. This paper compares the performance between the PDE and DL forecast for the thermal results. We show DL models are promising for simulation of the additive manufacturing process, and can be reliable alternatives for computationally-expensive FEM tools.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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