| Title |
Applying data mining technique to disassembly sequence planning: a method to assess effective disassembly time of industrial products |
| ID_Doc |
10066 |
| Authors |
Marconi, M; Germani, M; Mandolini, M; Favi, C |
| Title |
Applying data mining technique to disassembly sequence planning: a method to assess effective disassembly time of industrial products |
| Year |
2019 |
| Published |
International Journal Of Production Research, 57.0, 2 |
| DOI |
10.1080/00207543.2018.1472404 |
| Abstract |
Design for end-of-life and design for disassembly are enabling design strategies for the implementation of business models based on the circular economy paradigm. The paper presents a method for calculating the effective disassembly sequence and time for industrial products. Five steps support designers in defining liaisons and related properties and precedence among components with the aim to calculate the best disassembly sequence and time. The effective disassembly time is computed considering the actual conditions of a product and its components (e.g. deformation, rust and wear) using corrective factors. This aspect represents the main contribution to the state of the art in the field of design for disassembly. The corrective factors are derived from a specific data mining process, based on the observation of real de-manufacturing activities. The proposed approach has been used for calculating the disassembly times of target components in a washing machine and in a coffee machine. The case studies highlight the method reliability of both: definition of time-effective disassembly sequences and assessment of effective disassembly times. In particular, a comparison of experimental tests shows a maximum deviation of -6% for the electric motor of the washing machine and -3% for the water pump of the coffee machine. |
| Author Keywords |
design for disassembly; disassembly planning; data mining; estimated disassembly time; de-manufacturing; target disassembly |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000457968400017 |
| WoS Category |
Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science |
| Research Area |
Engineering; Operations Research & Management Science |
| PDF |
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