Towards a Numerical Approach of Finding Candidates for Additive Manufacturing-Enabled Part Consolidation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Part consolidation (PC) is one of the typical design freedoms enabled by additive manufacturing (AM) processes. However, how to select potential candidates for PC is rarely discussed. This deficiency has hindered AM from wider applications in industry. Currently available design guidelines are based on obsolete heuristic rules provided for conventional manufacturing processes. This paper first revises these rules to take account of AM constraints and lifecycle factors so that efforts can be saved and used at the downstream detailed design stage. To automate the implementation of these revised rules, a numerical approach named PC candidate detection (PCCD) framework is proposed. This framework is comprised of two steps: construct functional and physical interaction (FPI) network and PCCD algorithm. FPI network is to abstractly represent the interaction relations between components as a graph whose nodes and edges have defined physical attributes. These attributes are taken as inputs for the PCCD algorithm to verify conformance to the revised rules. In this PCCD algorithm, verification sequence of rules, conflict handling, and the optimum grouping approach with the minimum part count are studied. Compared to manual ad hoc design practices, the proposed PCCD method shows promise in repeatability, retrievability, and efficiency. Two case studies of a throttle pedal and a tripod are presented to show the application and effectiveness of the proposed methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it