On sustainable design and manufacturing for the footwear industry – Towards circular manufacturing
Bibliographic record
Abstract
The global fashion industry is facing carbon footprint issues, but technological innovations are helping to improve its performance and environmental efficiency in terms of footwear manufacturing. This paper explores how Design for Additive Manufacturing (DfAM), Design for Assembly (DfA), and Design for Disassembly (DfD) strategies, along with Additive Manufacturing's (AM) capability to produce intricate parts, can contribute to the fashion industry's shift towards a Circular Manufacturing model. The focus is on footwear manufacturing and its carbon footprint issues. The proposed additively manufactured shoe design utilizes Polyamide 12 and Thermoplastic polyurethane as feedstock, featuring a glueless mechanical assembly system based on a snapfit. Notably, the upper part of the shoe incorporates a variable lattice structure to ensure flexibility in different areas. Finite Element Analysis (FEA) demonstrates that the snapfit assembly exceeds the industry standard's minimum disassembly force requirement. Additionally, an optimization algorithm for the variable lattice structure results in a 34% mass reduction while maintaining the desired Young's modulus in each shoe zone. This design approach aligns with the footwear industry's sustainability goals, aiming to reduce environmental impact and enhance product durability. The study successfully developed a strategy to implement AM for sustainable shoe fabrication.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".