Additive manufacturing infill optimization for automotive 3D-printed ABS components
Bibliographic record
Abstract
Purpose Lightweighting of components in the automotive industry is a prevailing trend influenced by both consumer demand and government regulations. As the viability of additively manufactured designs continues to increase, traditionally manufactured components are continually being replaced with 3D-printed parts. The purpose of this paper is to present experimental results and design considerations for 3D-printed acrylonitrile butadiene styrene (ABS) components with non-solid infill sections, addressing a large gap in the literature. Information published in this paper will guide engineers when designing fused deposition modeling (FDM) ABS parts with infill regions. Design/methodology/approach Uniaxial tensile tests and three-point bend tests were performed on 12 different build configurations of 20 samples. FDM with ABS was used as the manufacturing method for the samples. Failure strength and elastic modulus were normalized on print time and specimen mass to quantify variance between configurations. Optimal infill configurations were selected and used in two automotive case study examples. Findings Results obtained from the uniaxial tensile tests and three-point bend tests distinctly showed that component strength is highly influenced by the infill choice selected. Normalized results indicate that solid, double dense and triangular infill, all with eight contour layers, are optimal configurations for component regions experiencing high stress, moderate stress and low stress, respectively. Implementation of the optimal infill configurations in automotive examples yielded equivalent failure strength without normalization and significantly improved failure strength on a print time and mass normalized index. Originality/value To the best of the authors’ knowledge, this is the first paper to experimentally determine and quantify optimal infill configurations for FDM ABS printed parts. Published data in this paper are also of value to engineers requiring quantitative material properties for common infill configurations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".