A Method for Improving the Process and Cost of Nondestructive Disassembly
Bibliographic record
Abstract
The ever-increasing global environmental concerns demand that strong emphasis be given to the decision-making process pertaining to the strategy of design for nondestructive disassembly during the product evaluation stage. The ultimate objective is to considerably increase the percentage of product components and materials suitable for recycling, recovery, and/or reuse. In this context, this paper proposes a method for improving the nondestructive disassembly of a final product. This method analyzes the nondestructive disassembly by determining a disassembly interference matrix, feasible disassembly sequences, and improved nondestructive disassembly sequences. The innovative element of the nondestructive disassembly method proposed in this paper is integrating the generated conceptual design solutions for a given technical device with a software package developed for determining its improved disassembly sequence embedded within a 3D CAD platform. The developed procedure is based on information obtained from a 3D CAD model of a product, such as geometric constraints, automatic identification of fasteners and components, determination of component-to-component and component-to-fastener connection graphs, and AND/OR logic operations. The goal is for product designers to predict, evaluate, and define improved disassembly sequences while minimizing the cost of disassembly operations as early in the design stage as possible after a CAD model of the product becomes available. The applicability of the integrated method for determining the improved disassembly sequence is presented through an illustrative example.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".