Relating additive and subtractive processes in a teleological and modular approach
Bibliographic record
Abstract
Purpose The purpose of this paper is to relate additive manufacturing (AM) and machining (CNC) synergistically in a modular approach in the design and manufacturing domains, to generate value for end‐users and manufacturers (a teleological system). Design/methodology/approach The research methodology decomposes a part into modules, by employing a teleological systems theory approach paired with principles of modular design. Modules are manufactured with either additive manufacturing (fused deposition modeling, FDM) or machining (CNC). Process selection is determined by a decision‐making framework that quantifies strength and weakness comparisons of FDM and CNC machining processes, accomplished using the analytic hierarchy process (AHP). Findings The developed methodology and decision‐making framework is successfully applied to the design and manufacturing of a large, complex V6 engine section sand casting pattern. This case study highlights the merits of the research. Research limitations/implications The research assumes that the processes being considered are capable of meeting the product functional requirements. The proposed methodology can be extended to evaluate additional processes. Practical implications Value is assessed in this research relative to: time and cost opportunities, managing knowledge limitations of a process by leveraging hybrid options, and aligning design and manufacturing to create a product that accomplishes the goals of the end‐user (teleological effectiveness). Originality/value Utilizing the AHP process and a teleological perspective are new, and proven effective, approaches in relating additive and subtractive processes in a hybrid approach with end‐user perspectives. The research demonstrates a systematic methodology to quantify additive and subtractive process selection.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".