Interaction between Time Dependent Exposure Strategies and Part Positioning within Selective Laser Melting Process of Plastics
Bibliographic record
Abstract
The selective laser melting of polymer powder is, for rapid prototyping applications, a well-established technology, although a lack in basic process knowledge occurs. Considering demands of series production the selective laser melting technique of polymers is faced with various challenges concerning processable material systems, process strategies and part properties. Consequently, basic research is necessary to understand and optimize processes to shift from rapid prototyping to rapid manufacturing of small lot sized series. Based on basic research the high potential of selective laser melting for the production of complex parts without any tools can be opened up. For the derivation of part quality increasing process strategies, knowledge about interactions between sub-processes of selective laser melting and resulting part properties is necessary. The selective laser melting of polymers consists of three major sub-processes: Powder coating, energy input, material consolidation. According to the interaction of sub-processes, resulting temperature fields during the selective laser melting process determine the part properties by changing micro structural pore number and distribution. Beneath absolute temperatures the time-dependency of the thermal fields also influences the porosity of molten parts. Present process strategies tend to decrease building time by increasing scanning speed and laser power. Although the absolute energy input into the material is constant for increasing scanning speed and laser power in the same ratio, time dependent material effects are neglected. The heating rate is a combined parameter derived from absolute temperature and time. Within the paper the authors analyze the basic interactions between different heating rates and part properties (e. g. mechanical strengths). Furthermore, the part positioning is taken into account. Due to the part positioning within the building chamber different shapes of cross sections appear even for equal part geometries. The authors estimate an interaction between exposed cross section and applied speed of energy input, due to heat accumulating effects. Therefore specimens produced with different heating rates are analyzed with imaging technologies as well as mechanical tests. Based on the done basic investigations new heating rate dependent process strategies can be established considering time dependent material behavior.
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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 | 0.001 | 0.001 |
| 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 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".