Additive Manufacturing Experimental Infill Testing and Optimization for Automotive Lightweighting
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Lightweighting of vehicles in the automotive industry is one of the most prevalent trends currently underway; influenced by government regulation and consumer demand. The reduction in vehicle mass of the next generation automobile offers increased dynamic performance, reduced fuel consumption, and potential component cost reduction. Development in composite materials, numerical methods, part consolidation, and advanced high strength metals represent a selection of the strategies being utilized for lightweighting. Additive manufacturing (AM) is a family of rapidly developing technology that is seeing use in the automotive industry both in the development and production stages. Fused deposition modelling (FDM) printed parts offer designers increased freedom, at a reduced weight, in comparison to conventionally fabricated parts as internal sections that are hollow, sparsely filled, or composed of a lattice structure can be realized instead of the traditional solid infill matrix.</div><div class="htmlview paragraph">This paper investigates the gap in available knowledge on FDM printing infill designs, examining macro material properties for design considerations as a function of both mass and print time. Experimental data of prevalent infill patterns and structural correlation to contour layer effect are shown. An optimal configuration for both the minimization of mass and minimization of print time are presented, providing tangible structural data to designers that can be utilized in both structural and semi-structural applications. A set of examples is presented showcasing the applicability of FDM printed designs in an automotive production application and in an automotive product development application. Results indicate that the adoption of optimal infill patterns for FDM printed components will create new lightweighting applications in the automotive industry.</div></div>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".