Concurrent Modelling and Experimental Investigation of Material Properties and Geometries Produced by Projection Microstereolithography
Bibliographic record
Abstract
Projection microstereolithography additive manufacturing (PµSLA-AM) systems utilize free radical photopolymerization to selectively transform liquid resins into accurate and complex, shaped, solid parts upon UV light exposure. The material properties are coupled with geometrical accuracy, implying that optimizing one response will affect the other. Material properties can be enhanced by the post-curing process, while geometry is controlled during manufacturing. This paper uses designed experiments and analytical curing models concurrently to investigate the effects of process parameters on the green material properties (after manufacturing and before applying post curing), and the geometrical accuracy of the manufactured parts. It also presents a novel accumulated energy model that considers the light absorbance of the liquid resin and solid polymer. An essential definition, named the irradiance affected zone (IAZ), is introduced to estimate the accumulated energy for each layer and to assess the feasibility of the geometries. Innovative methodologies are used to minimize the effect of irradiance irregularities on the responses and to characterize the light absorbance of liquid and cured resin. Analogous to the working curve, an empirical model is proposed to define the critical energies required to start developing the different material properties. The results of this study can be used to develop an appropriate curing scheme, to approximate an initial solution and to define constraints for projection microstereolithography geometry optimization algorithms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".