An automated decision support system for the identification of additive manufacturing redesign candidates
Bibliographic record
Abstract
As additive manufacturing (AM) continues to mature, so does the need for a quick and effective method of determining how it should be applied in product design.In the past, these methods were naturally developed and passed on as tacit knowledge.However, with the rapid advancement of AM technologies, identifying parts which are eligible for AM as well as gaining insight on what value it may add to a product needs to be modelled in an objective and transferrable way.In this work, a framework for determining the candidacy of a part or assembly for AM is developed based on its economic feasibility and potential for AM-specific benefits.A set of selection criteria is created with the goal of fast-screening in mind; that is, the criteria is linked to data which can be automatically extracted from CAD models and resource planning databases.These selection criteria are used in conjunction with the candidacy framework to develop an automated decision support system for the identification of additive manufacturing redesign candidates.The decision support system is implemented in the form of a software which can be connected to existing CAD software for the quick identification of parts/assemblies that can be re-designed to be suitable for AM fabrication.Several case studies were conducted to validate the effectiveness of this decision support system.i ADML Additive Design and Manufacturing Lab AJAX Asynchronous JavaScript and XML AM Additive manufacturing AMK Additive manufacturing knowledge API Application programming interface ASTM American Society for Testing and Materials CAD Computer aided design DfAM Design for additive manufacturing DSS Decision support system
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".