Initial analysis of cost, energy and carbon dioxide emissions in single point incremental forming – producing an aluminium hat
Bibliographic record
Abstract
In this paper, an initial analysis of cost, energy and carbon dioxide (CO2) emissions that occur in producing a unique aluminium hat using single point incremental forming (SPIF) for two scenarios is performed. The aluminium hat was custom designed and made from Al-3003 O and is formed using a custom steel alloy SPIF tool and vertical computer numeric control (CNC) mill. The second scenario (S2) involved doubling the feed rate and step down increment of the first scenario (S1), as well as using an eco-benign lubricant. The cost and energy used for the SPIF process without labour were found to be $4.48 and 4580 kJ (1.27 kWh) for S1 and $4.10 and 1420 kJ (0.39 kWh) for S2, respectively. The respective direct electrical energy required for making the hat was only 16% and 27% of the total required process energy for S1 and S2. Using virgin or traditional emission intensity inputs for the tool, lubricant, workpiece and energy, the embodied CO2 from the process was found to be 4.48 kg CO2e for S1. However, using 33% recycled aluminium, an eco-benign lubricant and a remanufactured tool resulted in an embodied CO2 of 3.24 kg CO2e or a 28% CO2 savings for the same process parameters. Similarly, in S2, the embodied CO2 was found to be 4.28 kg CO2e for traditional inputs and 3.09 kg CO2e for modified inputs. Comparing S1 traditional and S2 modified, there is a reduction in energy use and CO2 by 69% and 31% accordingly. As expected, the stock material dominated the embodied CO2 and cost, but the energy consumed was the next highest contributor. Future work will consider optimal parameters for cost, energy and embodied CO2 minimisation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".