Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A big data based cost prediction method for remanufacturing end-of-life products

Ding, Zhouyang, Jiang, Zhigang, Liu, Ying ORCID:, Wang, Yan and Li, Congbo 2018. A big data based cost prediction method for remanufacturing end-of-life products. Procedia CIRP 72 , pp. 1362-1367. 10.1016/j.procir.2018.03.129

[thumbnail of A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products.pdf]
PDF - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview


Remanufacturing is considered as an important industrial process to restore the performance and function of End-of-Life (EOL) products to a like-new state. In order to help enterprises effectively and precisely predict the cost of remanufacturing processes, a remanufacturing cost prediction model based on big data is developed. In this paper, a cost analysis framework is established by applying big data technologies to interpret the obtained data, identify the intricate relationship of obtained sensor data and its corresponding remanufacturing processes and associated costs. Then big data mining and particle swarm optimization Back Propagation (BP) neural network algorithm are utilized to implement the cost prediction. The application of presented model is verified by a case study, and the results demonstrates that the developed model can predict the cost of the remanufacturing accurately allowing early decision making for remanufacturability of the EOL products.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Subjects: T Technology > TS Manufactures
Publisher: Elsevier
ISSN: 2212-8271
Date of First Compliant Deposit: 4 July 2018
Date of Acceptance: 31 March 2018
Last Modified: 02 May 2023 17:18

Citation Data

Cited 7 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics