A novel approach to recoil pad enhancement in rifles via topological design with material extrusion and SLA
Bibliographic record
Abstract
Excessive recoil in firearms reduces shooting accuracy, causes user discomfort, and increases mechanical wear over time. This creates a need for effective solutions that can improve recoil management. This study investigates the use of advanced lattice structures made from thermoplastic polyurethane (TPU) and resin to enhance vibration damping and impact absorption in firearm components. Four lattice geometries were used, including Voronoi, Weaire Phelan, Gyroid, and Kelvin Cell. These structures were fabricated using two additive manufacturing methods. Material extrusion was applied for TPU parts to provide flexibility and strength, while Stereolithography (SLA), a light-based resin curing technique, was used to produce intricate resin structures. These approaches highlight the adaptability of additive manufacturing in producing functional parts that offer both vibration control and impact resistance. The performance of each structure was evaluated through vibration damping in two modes, rapid impact testing, and compression strength assessments. TPU samples consistently outperformed resin in both damping and energy absorption. Voronoi TPU exhibited the highest damping ratio, improving by 331 percent in the first mode with a value of 1.25 and by 300 percent in the second mode with a value of 0.20 compared to the original solid part. Weaire Phelan TPU reached the highest impact energy absorption at 6.45 joules, which is an improvement of 18.14 percent. Gyroid TPU absorbed energy the fastest, reaching a peak of 5.4 joules in only 14 milliseconds. This study introduces a new design approach by applying lattice structures to recoil pads, enabling enhanced performance and a high degree of customization.
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.
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".