Electroforming on Additively Manufactured Mandrels
Bibliographic record
Abstract
Manufacturing industry faces recently a new challenge. Production of parts require, for various reasons, more and more low batch size production, sometimes even batch size one. Not only the batch size is reduced, but a given design has to be produced in lower on lower number too. If previously only prototyping required batch size one prodcution, the new trend of mass personalization requires it as well. Manufacturing technologies developed over the past decades are able to produce cost effectively very complex products in high series. Low batch series production is often not possible or very costly. Additive manufacturing is a promising answer to this challenge. Additive manufacturing equipment represent however, in case of metal printing, very high investment costs and usually significant post-processing after printing is needed. An alternative way to create complex 3D printed parts in low volumes could be to use electroforming. Electroforming is commonly used to form parts on a model or, as termed in industry, a mandrel. Using additive manufacturing to create this mandrel could be a viable and cost-efficient solution. Since electroforming require less energy and less expensive equipment, it can be potentially used to create precise 3D structures. In order to create a 3D shape using electroforming. Electroforming has its own challenges. As the thickness of the electroforming increases, voids build up and create a rough, uneven surface that has undesirable physical properties. The mass transfer at the micro-scale also changes the contribution of migration, diffusion and convection phenomena due to the scaling effect [1]. In order to produce complex and micro-scale features with acceptable quality, profile and characteristics, this paper discusses some significant factors and several methods for the improvement. By applying appropriate potential and rotation on the mandrel during the electroforming process, deposition thickness ranging from few to several 100 microns with very low surface roughness (few microns Ra) could be achieved. The deposit thickness was very consistent too (less than 1% variation of the complete part). The homogeneity of the thickness was further investigated for various geometries to determine the limitations of the methodology. [1] Angel, K., Tsang, H. H., Bedair, S. S., Smith, G. L., & Lazarus, N. (2018). Selective electroplating of 3D printed parts. Additive Manufacturing, 20, 164–172. doi: 10.1016/j.addma.2018.01.006
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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 | 0.000 | 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) | 0.002 | 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".