Research on data mining technology for the connotation and measurement of uncertainty for reassembly dimensions. (2018)

• Record Type: Journal Article
• Authors: Liu, Conghu; He, Kang; Zhang, Yingfeng; Liu, Changyi
• Is Part Of: International journal of high performance systems architecture. Volume 8:Number 1/2(2018)
• Page Start: 13
• Page End: 21
• Publication Date: 2018
• Subjects: remanufacturing -- data mining -- uncertainty -- entropy; Computer architecture -- Periodicals; Computer systems -- Periodicals; High performance computing -- Periodicals; 004.205
• Languages: English
• ISSNs: 1751-6528

Abstract

The uncertainty of remanufactured parts is a key factor in the stability of remanufacturing systems. Therefore, the purpose of this paper is to identify these uncertainties and measure them to improve the optimisation management level of remanufacturing production process. Contrasting the ideal dimensional accuracy, manufactured dimensional accuracy and remanufactured dimensional accuracy, we analyse the connotation of uncertainty for reassembly dimensions. We construct the uncertainty measurement model for reassembly dimensions to realise quantitative measurement by entropy. So the coupling mechanism of uncertainty for reassembly dimensions is studied and the corollary is in conformity with the reality. It can use data mining technology to optimise remanufacturing process management. Finally, the feasibility and effectiveness of the model are verified in grading selection of remanufacturing enterprise parts. This research provides support for the uncertain optimisation decision for lean remanufacturing from both theoretical and practical aspects by uncertain data mining techniques.