Rules and methods for exploring and utilizing additive manufacturing-enabled part consolidation potentials in product redesign
Bibliographic record
Abstract
Part consolidation (PC) is an effective design technique to reduce part count and simplify product architecture. Through PC, it brings numerous benefits such as reduced assembly operation, decreased supply chain management cost, and increased structural reliability. Consolidation of parts, however, may lead to increased manufacturing difficulty in terms of complex geometry and material composition. Constrained by the capability of conventional manufacturing methods (e.g. machining and casting), PC is only applicable to components having simple geometries, no relative motion, no material variance, and no blockage of assembly access of others. As additive manufacturing (AM) evolves into an end-of-use product fabricating method, such constraints of PC have been largely relaxed, and PC has become one of the primary motivations of using AM. However, the rules and methods for exploring such emerging PC potentials are obsolete, and the understanding of how to utilize these PC potentials in a general product redesign process is very limited. To fill these gaps, three contributions are made in this thesis. First, a methodological framework enabling full exploitation of AM-enabled PC potentials in product redesign is proposed. The framework is highlighted by three design flows: core flow, complementary flow, and innovative flow. The core flow is dedicated to screening, consolidating, and refining parts that are highly feasible for consolidation. The complementary flow is supplementary to the core flow to further investigate the slight consolidation potential for parts that are rejected by the core flow. The innovative flow works in parallel with the prior flows. It aims at enlarging the design solution space by advocating design for function throughout the synthesis process of working principle, product layout, design space, material, and architecture. Amongst the three proposed flows, the core flow is the primary focus of this research and has been thoroughly investigated and implemented. Second, new candidacy rules, principles, strategies, and tools to support the systematic and automatic identification of PC candidates are developed. Third, a functional entity-based design method is proposed to aid the transition from the candidacy assembly design to the functionally-equivalent consolidated design. In the end, a computer-aided design tool is developed to implement the proposed core flow, which paves the way for better exploring and utilizing AM-enabled PC potentials in the redesign process of a complex product.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".