Design of a decision support system for making informed decisions about selection of machines for manufacturing leather garments
Bibliographic record
Abstract
This study investigates the process of selecting sewing machines for the manufacturing of products from artificial leather. Despite the active development of technological solutions for automation, the task of choosing optimal equipment remains relevant, requiring additional tools that can provide a connection between scientific approaches and industrial conditions. This paper reports the results of designing an automated decision support system for the selection of sewing equipment, aimed at bridging the gap between theoretical models and production needs. The technological advancement is based on a three-level database structure. At the data storage level, a matrix-based database of equipment parameters was constructed, ensuring the consistency of information regarding technological operations, materials, and machine characteristics. At the logical level, a multifactor analysis algorithm was developed, utilizing the principles of graph theory, a binary matrix, and the linear programming method to select the optimal equipment model. The representation level is an interactive interface based on MS Excel (USA). Input parameters are selected by simply clicking on buttons with corresponding names (seam type, worker qualification, material properties, and thickness). The system automatically analyzes the database and generates a list of recommended equipment in a table format. Verification was carried out through a survey involving 30 participants (86.7% were representatives of the academic community). The results show that 93.3% of respondents noted the high speed of the simulator while 90.0% rated its practicality and 86.7% its convenience. At the same time, certain shortcomings were identified, outlining areas for further research: 23.3% of those surveyed highlighted the need to expand the database, and 16.7% emphasized the necessity of implementing a Ukrainian-language version. It was established that the designed system is a universal tool that combines educational and practical-production dimensions. Its implementation in the educational process will contribute to achieving a number of program learning outcomes
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".