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Defect prediction models to improve assembly processes in low-volume productions

Year: 2021

Type: article

Abstract: Assembly 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.

Source: Procedia CIRP

DOI: 10.1016/j.procir.2020.05.217

Authors: Elisa Verna, Gianfranco Genta, Maurizio Galetto, Fiorenzo Franceschini

Institution: Politecnico di Torino

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: Dipartimenti di Eccellenza, Ministero dell’Istruzione, dell’Università e della Ricerca