Assembly-Level Design for Additive Manufacturing: Issues and Benchmark
Bibliographic record
Abstract
The emerging additive manufacturing (AM) technology works in a layer-wise fashion which makes it possible to manipulate material distribution and composition. The resulting effects are reflected on the potential of innovative shape design, consolidated assembly, optimized topology, and functionally graded material. These new characteristics force designers to rethink about how to make a better engineering design. However, existing design theory and methodology cannot take these potentials provided by AM into account. To fill this void, various design for additive manufacturing (DFAM) approaches are reported. Unfortunately, majority of them focused on part-level redesign without potential of being extended to assembly-level applications. In order to shed a light into this emerging field, an overview of current assembly-level DFAM is summarized in this paper. After that, existing issues including the absent analysis of AM’s impact on conceptual design, the lack of explicit functional analysis method, the shortage of decision-making support for part consolidation, the deficiency of functional reasoning approaches to generate AM-enabled features, and the scarcity of integrating manufacturing and assembly knowledge into design stage are analyzed and discussed. However, it seems that addressing these issues is such a large scope that collaborative efforts are in need from both design and manufacturing communities. Therefore, this paper serves as a call to action for the research community to establish a comprehensive assembly-level/ product-level DFAM method to realize product evolution. As an initial benchmark, authors propose a three-stage design methodology on the basis of the Systematic Design approach. In the presented framework, functional analysis, part consolidation, and structural optimization with process knowledge integration are much highlighted. Moreover, a simple redesign case study is exemplified to clarify existing issues and how the benchmark method works. In the end, this paper is wrapped up with future research.
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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".