Development of Intertwined Infills to Improve Multi-Material Interfacial Bond Strength
Bibliographic record
Abstract
Abstract Recently, the availability of various materials and ongoing research in developing advanced systems for multi-material additive manufacturing (MMAM) have opened doors for innovation in functional products. One major concern of MMAM is the strength at the interface between materials. This article hypothesizes overlapping and interlacing materials to enhance the bonding strength. To test this hypothesis, we need a computer-aided manufacturing (CAM) tool that can process the overlapped material regions. However, existing computational tools lack key multi-material design processing features and have certain limitations in making full use of the material information, which restricts the testing of our hypothesis. Therefore, this research also develops a new MMAM slicing framework that efficiently identifies the boundaries for materials to develop different advanced features. By modifying a ray tracing technology, we develop layered-depth material images (LDMI) to process the material information from computer-aided design (CAD) models for slicing and process planning. Each sample point in the LDMI has associated material and geometric properties that are used to identify the multi-material regions. Based on the material information in each slice, interlocking joint (T-Joint) and interlacing infill are generated in the regions with multiple materials. Tensile tests have been performed to verify the enhancement of mechanical properties by the use of overlapping and interlacing materials.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".