Design for disassembly is a key enabling strategy for the development of new business models based on the Industry 4.0 and circular economy paradigms. This paper attempts to define a method, based on Data Mining, for modelling disassembly data from large amount of records collected through the observation of de-manufacturing activities. The method allows to build a repository to characterize the disassembly time of joining elements (e.g. screws, nuts) considering different features and conditions. The approach was preliminary tested on a sample of 344 records for nuts disassembly retrieved by in-house tests. Disassembly time and corrective factors were assessed including the analysis of probability distribution function and standard deviation for each feature (i.e. disassembly tool).

Big data analysis for the estimation of disassembly time and de-manufacturing activity / Favi, C.; Marconi, M.; Mandolini, M.; Germani, M.. - 90:(2020), pp. 617-622. (Intervento presentato al convegno CIRP LCE 2020) [10.1016/j.procir.2020.01.072].

Big data analysis for the estimation of disassembly time and de-manufacturing activity

Favi C.
;
2020-01-01

Abstract

Design for disassembly is a key enabling strategy for the development of new business models based on the Industry 4.0 and circular economy paradigms. This paper attempts to define a method, based on Data Mining, for modelling disassembly data from large amount of records collected through the observation of de-manufacturing activities. The method allows to build a repository to characterize the disassembly time of joining elements (e.g. screws, nuts) considering different features and conditions. The approach was preliminary tested on a sample of 344 records for nuts disassembly retrieved by in-house tests. Disassembly time and corrective factors were assessed including the analysis of probability distribution function and standard deviation for each feature (i.e. disassembly tool).
2020
Big data analysis for the estimation of disassembly time and de-manufacturing activity / Favi, C.; Marconi, M.; Mandolini, M.; Germani, M.. - 90:(2020), pp. 617-622. (Intervento presentato al convegno CIRP LCE 2020) [10.1016/j.procir.2020.01.072].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2881188
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