Machine acceleration time series prediction for dimensional accuracy of 3D printed parts
Bibliographic record
Abstract
This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional (3D) printing, particularly focusing on extrusion technology. Our primary objective was to develop a long short-term memory (LSTM) network capable of assessing this impact. We conducted an extensive analysis involving 12 distinct infill patterns, collecting time-series data to examine their effects on the acceleration of the printer’s bed. The LSTM network was trained using acceleration data from the adaptive cubic infill pattern, while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy. This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model. Specifically, the LSTM model was devised to predict the acceleration of a fused deposition modeling (FDM) printer using data from the adaptive cubic infill pattern. Rigorous testing yielded a root mean square error (RMSE) of 0.007144, reflecting the model’s precision. Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern, resulting in an RMSE of 0.007328. Notably, the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network (RNN) in predicting machine acceleration data. The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".