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EnglishAssembly processes in low-volume productions, i.e., single-units or small-sized-lots, are often characterized by a high level of customization and complexity. As a consequence, the scarcity of historical data and the difficulty in applying standard statistical techniques make process control extremely challenging. Accordingly, identifying effective diagnostic tools plays a key role in such productions. This paper proposes an innovative method for identifying critical workstations in assembly processes based on defect prediction models. Starting from the level of complexity in terms of assembly process and design, the method allows identifying the workstations whose defectiveness deviates, at a certain confidence level, from the predicted average value. Once the causes leading to significant nonconformities have been detected, appropriate corrective actions may be promptly undertaken to improve the process. An example of implementation of the method in wrapping machines production is presented and discussed.
Defect prediction models to improve assembly processes in low-volume productions / Verna, Elisa; Genta, Gianfranco; Galetto, Maurizio; Franceschini, Fiorenzo. - (In corso di stampa). ((Intervento presentato al convegno 8th CIRP Conference of Assembly Technology and Systems tenutosi a Athens (Greece) nel 29 Settembre - 1 Ottobre 2020.
| Titolo: | Defect prediction models to improve assembly processes in low-volume productions |
| Autori: | |
| Data di pubblicazione: | Being printed |
| Serie: | |
| Abstract: | Assembly processes in low-volume productions, i.e., single-units or small-sized-lots, are often c...haracterized by a high level of customization and complexity. As a consequence, the scarcity of historical data and the difficulty in applying standard statistical techniques make process control extremely challenging. Accordingly, identifying effective diagnostic tools plays a key role in such productions. This paper proposes an innovative method for identifying critical workstations in assembly processes based on defect prediction models. Starting from the level of complexity in terms of assembly process and design, the method allows identifying the workstations whose defectiveness deviates, at a certain confidence level, from the predicted average value. Once the causes leading to significant nonconformities have been detected, appropriate corrective actions may be promptly undertaken to improve the process. An example of implementation of the method in wrapping machines production is presented and discussed. |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| PROCIR_CATS_2020_VernaGentaGalettoFranceschini.pdf | Articolo completo | 2. Post-print / Author's Accepted Manuscript | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2832874
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