A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing-Reference-Cited by-同舟云学术

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A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing

Published:2022-04-07 Issue: Volume: Page:1-24
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Liu Changchun¹^ORCID,Zhu Haihua¹,Tang Dunbing¹,Nie Qingwei¹,Li Shipei¹,Zhang Yi¹,Liu Xuan²

Affiliation:

1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, People’s Republic of China

2. Department of Application and Research, Sino-German Intelligent Manufacturing Research Institute, Nanjing, People’s Republic of China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Key Research and Development Program of Jiangsu Province

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2056860

Reference41 articles.

1. Real-time prediction of order flowtimes using support vector regression

2. An analytical model for service level and tardiness in a single machine MTO production system

3. Factory Cycle-Time Prediction With a Data-Mining Approach

4. A novel approach for product makespan prediction in production life cycle

5. Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

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