Computer-based data processing approaches to production scrap management
Artykuł w czasopiśmie
MNiSW
70
Lista 2024
| Status: | |
| Autorzy: | Wójcik Łukasz, Gola Arkadiusz, Pizoń Jakub |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 3 |
| Wolumen/Tom: | 22 |
| Strony: | 108 - 120 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 30 września 2026 |
| Abstrakty: | angielski |
| The aim of this study was to design and empirically validate a predictive approach for classifying scrap types and forecasting scrap volume in brake calliper remanufacturing, oriented toward operational decision-making. We addressed three research questions: (1) which “red-flag” defects most strongly increase the risk of high scrap (“Large”); (2) whether scrap category is driven by the accumulation of multiple defects or by single catastrophic defects; and (3) whether logistic regression provides sufficient performance and interpretability for industrial use, or whether more complex machine-learning models are justified. The analysis used real industrial data (435 cases, 34 defect-related features with discrete values: 0/1 and, in some cases, counts). The baseline model (logistic regression) achieved 79% accuracy and an AUC of 0.773, confirming its practical usefulness in a production environment. Coefficient analysis indicated, among others, that the defect “power-supply leakage” is a strong predictor of the “Norm” class (OR ≈ 4.64 for “Norm” vs. “Large”), while defect-profile analysis revealed an accumulation trajectory (mean 7.3 defects for “Large” vs. 3.5 for “Norm”), distinct from a catastrophic trajectory (immediate disqualification). The study shows that interpretable models can support decision processes in remanufacturing, reducing scrap and supporting circular-economy and Industry 4.0 goals |
