Structural Design Strategies for the Production of Internal Combustion Engine Components by Additive Manufacturing: A Case Study of a Connecting Rod
Bibliographic record
Abstract
Topology optimization and lattice design strategies are excellent tools within the design for additive manufacturing (DfAM) workflow as they generate structurally optimal, lightweight, and complex features often difficult to produce by conventional manufacturing methods. Moreover, topology optimization approaches are quickly evolving to accommodate AM-related processes and geometric constraints. In this study, the re-design of the connecting rod of an internal combustion engine (ICE) is explored by topology optimization and lattice structures. In both topology optimization and lattice design, the objective is to maximize their structural performances while constraining material usage. Structural analyses are carried out on the optimized topologies to compare their mechanical performances with a benchmark design. Results show that the redesign of the connecting rod through topology optimization alone can realize 20% material savings with only a 5% reduction in the factor of safety. However, the combination of topology optimization and lattice structure design can result in over 50% material savings with a 21–26% reduction in the factor of safety. For manufacturability, the fast-predictive inherent strain model shows the designs through topology optimization and lattice design gives the lowest process-induced deformations before and after support structure removal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".