A knowledge resources fusion method based on rough set theory for quality prediction-Reference-Cited by-同舟云学术

A knowledge resources fusion method based on rough set theory for quality prediction

Published:2019-06 Issue: Volume:108 Page:104-114
ISSN:0166-3615
Container-title:Computers in Industry
language:en
Short-container-title:Computers in Industry

Author:

Yin Yanchao,Zhang Litong,Liao Weizhi,Niu Hongwei,Chen Fuzhao

Funder

National Key R&D Program of China

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference40 articles.

1. Industrial additive manufacturing: a manufacturing systems perspective;Eyers;Comput. Ind.,2017

2. Tool-path planning with constraint of cutting force fluctuation for curved surface machining;Ma;Precis. Eng.,2018

3. A progressive approach to joint monitoring of process parameters;Zafar;Comput. Ind. Eng.,2018

4. Production and replacement policies for a deteriorating manufacturing system under random demand and quality;Ouaret;Eur. J. Oper. Res.,2018

5. Application of regression and artificial neural network analysis in modelling of surface roughness in hard turning of AISI 52100 steel;Maheshwera;Proceedings,2018

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Applications of Rough Set Theory in Demand Side Management of Electrical Power Industry: A Review;2024 IEEE 4th International Conference in Power Engineering Applications (ICPEA);2024-03-04

2. Interpretable Multiattribute Predictive Analysis Model Based on Rough Fuzzy Sets and Logistic Regression;IEEE Transactions on Fuzzy Systems;2023-10

3. Dynamic assessment and system dynamics simulation of safety risk in whole life cycle of coal mine;Environmental Science and Pollution Research;2023-04-15

4. A lower approximation based integrated decision analysis framework for a blockchain-based supply chain;Computers & Industrial Engineering;2023-03

5. Decision making in the manufacturing environment using the technique of precise order preference;Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi;2023