Intelligent manufacturing quality prediction model and evaluation system based on big data machine learning

被引:17
作者
Li, Xianwang [1 ]
Huang, Zhongxiang [1 ]
Ning, Wenhui [2 ]
机构
[1] Guangxi Univ, Coll Mech Engn, Guangxi Key Lab Mfg Syst & Adv Mfg Technol, Nanning 530000, Guangxi, Peoples R China
[2] Chongqing City Vocat Coll, Dept Informat & Intelligent Engn, Chongqing 402160, Peoples R China
关键词
Intelligent manufacturing; Quality prediction; Evaluation system; Machine learning; Neural network algorithm; Big data; CONTEXT;
D O I
10.1016/j.compeleceng.2023.108904
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This study addresses the challenges in the development of intelligent manufacturing by constructing a quality prediction model and evaluation system. To build the quality prediction model and evaluation system, we effectively investigated the construction of the intelligent manufacturing quality prediction model and evaluation system using extreme learning machine neural network and particle swarm optimization algorithms, combined with a big data machine learning system. The prediction results of the system were experimentally analysed to verify its performance. The experimental data showed that the prediction results of the system for each evaluation index of product quality were consistent with the actual product quality detection results. Importantly, the prediction results of the system can meet the requirements and are of great significance to the development of intelligent manufacturing.
引用
收藏
页数:11
相关论文
共 25 条
[1]  
Chien CF., 2018, Management Review, V37, P105
[2]   Hint: harnessing the wisdom of crowds for handling multi-phase tasks [J].
Fang, Yili ;
Chen, Pengpeng ;
Han, Tao .
NEURAL COMPUTING & APPLICATIONS, 2023, 35 (31) :22911-22933
[3]   Digital twin-based sustainable intelligent manufacturing: a review [J].
He, Bin ;
Bai, Kai-Jian .
ADVANCES IN MANUFACTURING, 2021, 9 (01) :1-21
[4]   Machine learning and deep learning [J].
Janiesch, Christian ;
Zschech, Patrick ;
Heinrich, Kai .
ELECTRONIC MARKETS, 2021, 31 (03) :685-695
[5]   Factories of the future: challenges and leading innovations in intelligent manufacturing [J].
Jardim-Goncalves, Ricardo ;
Romero, David ;
Grilo, Antonio .
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2017, 30 (01) :4-14
[6]   Smart manufacturing [J].
Kusiak, Andrew .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2018, 56 (1-2) :508-517
[7]   Applications of artificial intelligence in intelligent manufacturing: a review [J].
Li, Bo-hu ;
Hou, Bao-cun ;
Yu, Wen-tao ;
Lu, Xiao-bing ;
Yang, Chun-wei .
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 2017, 18 (01) :86-96
[8]   Multi-chain and data-chains partitioning algorithm in intelligent manufacturing CPS [J].
Li, Suisheng ;
Xiao, Hong ;
Qiao, Jingwei .
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2021, 10 (01)
[9]  
Li Y, 2022, Kinet. Mech. Eng., V3, P1
[10]   Analysis and research on intelligent manufacturing medical product design and intelligent hospital system dynamics based on machine learning under big data [J].
Liu, Zongxin ;
Pu, Jiaozi .
ENTERPRISE INFORMATION SYSTEMS, 2022, 16 (02) :193-207