Open-source 3-D printing materials database generator
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
Purpose This study aims to apply an open-source approach to protect the 3D printing industry from innovation stagnation due to broad patenting of obvious materials. Design/methodology/approach To do this, first an open-source implementation of the first five conditions of an open-source algorithm developed to identify all obvious 3-D printing materials was implemented in Python, and the compound combinations of two and three constituents were tested on ten natural and synthetic compounds. The time complexity for combinations composed of two constituents and three constituents is determined to be O(n 2 ) and O(n 3 ), respectively. Findings Generating all combinations of materials available on the Chemical Abstracts Services (CAS) registry on the fastest processor on the market will require at least 73.9 h for the latter, but as the number of constituents increases the time needed becomes prohibitive (e.g. 3 constituents is 1.65 million years). To demonstrate how machine learning (ML) could help prioritize both theoretical as well as experimental efforts a three-part biomaterial consisting of water, agar and glycerin was used as a case study. A decision tree model is trained with the experimental data and is used to fill in missing physical properties, including Young's modulus and yield strength, with 84.9 and 85.1% accuracy, respectively. Originality/value The results are promising for an open-source system that can theoretically generate all possible combinations of materials for 3-D printing that can then be used to identify suitable printing material for specific business cases based on desired material properties.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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