A Study of Design Fixation Related to Additive Manufacturing
Bibliographic record
Abstract
This study aims to understand the effect of additive manufacturing (AM) on design fixation. Whereas previous research illustrates the positive aspects of AM, the overarching hypothesis of this work is that it might also have negative effects with respect to conventional manufacturability. In this work, participants from two groups, a design for conventional manufacturing (DfCM) group, and a design for additive manufacturing (DfAM) group, were asked to design a basic product. Then, a second iteration of the design asked both groups to design for conventional processes, and to include subtractive and formative methods like machining and casting, respectively. Findings showed that the DfAM fixated on nonproducible manufacturing features and produced harder to conventionally manufacture designs, even when told specifically to DfCM. There was also evidence that the complex designs of the DfAM group limited their modeling success and seemed to encourage them to violate more design constraints. This study draws attention to the negative effect of AM knowledge on designers and provides motivation for treatment methods. This is important if AM is used in prototyping or short run production of parts that are slated for conventional manufacturing later. The issue of design fixation is not a problem if AM is the final manufacturing method—a more common practice nowadays. This work suggests that one should consider the possibility of fixation in design environments where AM precedes larger volume conventional manufacturing.
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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 | 0.001 | 0.001 |
| 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".