Investigation of additive manufacturing process parameters for sustainability to optimize energy and material consumption
Bibliographic record
Abstract
Additive Manufacturing (AM) offers many advantages to make objects over traditional subtractive manufacturing methods. For example, complex geometries can be easily fabricated, and light weight parts can be formed while maintaining the parts strength for the low carbon footprint, low material consumption and waste. But there are few areas in AM to improve i.e., sustainability, reliability, productivity, robustness, material diversity and part quality. Life Cycle Assessment studies have identified that the AM printing stage has a big impact on the life cycle sustainability of 3D printed products. AM building parameters can be properly selected to improve the sustainability of AM. This thesis presents an investigation of the Fused Deposition Modelling (FDM) process parameters for sustainability i.e., to reduce the energy and material consumption. Investigated parameters include the printing layer height, number of shells, material infilling percentage, infilling type, and building orientation. The impact of these parameters on the energy consumption, part weight, scrap weight and production time has been studied. The study uses both simulation and experimental methods to improve the accuracy of results. Taguchi Design of Experiments approach and statistical analysis tools are used to find optimal FDM parameter settings for sustainability. The building orientation and layer height have been found as major influencers on the energy consumption, part weight, scrap weight, and production time, whereas the number of shells, infilling type and infill percentage have the less impact. It is concluded that the building orientation and layer height can be optimized to reduce the energy and material consumption. It is found that the energy consumption is proportional to production time. The significance of this research lies on the factor that it investigates five AM process parameters at 3 levels. Models formulated in this research can be easily extended to other AM processes.
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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".