| Title |
A hybrid approach of rough set and case-based reasoning to remanufacturing process planning |
| ID_Doc |
8149 |
| Authors |
Jiang, ZG; Jiang, Y; Wang, Y; Zhang, H; Cao, HJ; Tian, GD |
| Title |
A hybrid approach of rough set and case-based reasoning to remanufacturing process planning |
| Year |
2019 |
| Published |
Journal Of Intelligent Manufacturing, 30.0, 1 |
| DOI |
10.1007/s10845-016-1231-0 |
| Abstract |
Remanufacturing, a process returning used products to at least as good as new condition, is increasingly recognized as an important part of the circular economy. Since returned used components for remanufacturing have varying conditions and different defects, remanufacturing is very time-consuming and labor-intensive. There is an urgent need to reuse knowledge generated from existing parts remanufacturing to rapidly create sound process planning for the new arrival of used parts. A hybrid method combing rough set (RS) and cased-based reasoning (CBR) for remanufacturing process planning is presented in this paper. RS is employed for features reduction and rapid determination of features' weights automatically, and CBR is utilized to calculate the similarity of process cases to identify the most suitable solution effectively from case database. The application of the methodology is demonstrated in an example of remanufacturing process for a saddle guide. The results indicated that the quality of remanufactured products has been improved significantly. The method has been implemented in a prototype system using Visual Studio 2010 and Microsoft SQL Server2008. The results suggested that the hybrid RS-CBR system is feasible and effective for the rapid generation of sound process planning for remanufacturing. |
| Author Keywords |
Remanufacturing; Process planning; Rough set; Case-based reasoning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000456588800004 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Manufacturing |
| Research Area |
Computer Science; Engineering |
| PDF |
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