A Framework for Waste Minimization in Laboratory-Scale Additive Manufacturing
Bibliographic record
Abstract
Although additive manufacturing (AM) offers a reduction in waste compared to conventional manufacturing processes, AM still generates non-negligible material losses and inefficiencies, from print failures to removable support structures. This research aims to propose a systematic Material Flow Analysis (MFA) framework tailored for AM laboratories. As a comprehensive approach, the framework maps the complete operational process from the filament to the final printed product, rather than focusing on specific printing activities. The case study is the Fused Filament Fabrication division of the Laboratory for Multiscale Mechanics (LM2 ) at Polytechnique Montreal. LM2 is dedicated to 3D printing polymers and composites using commercial printers and filaments. The MFA will be classified into two flow categories: fixed and variable waste. Printed products are highly customizable, which distinguishes their waste management strategies from traditional manufacturing operations. Fixed waste, such as disposable gloves and cleaning wipes, is quantifiable and repeatable in each production cycle. Therefore, the framework proposes direct alternatives (reuse/replace), depending on the CO2 eq. emission tradeoff. Variable waste, such as support structures, is design-dependent and varies based on the product. In laboratory-scale production, eco-design principles are non-systematic, and different user experience levels complexify their implementation. Therefore, as an alternative systematic approach, the framework proposes mechanical recycling as a key strategy that is assessable based on CO2 eq. emission and adaptable to different design outputs. Overall, the MFA aims to offer a guideline for waste management flow for a customizable manufacturing method at laboratory scale, identifying potential pathways towards a sustainable AM industry.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".