A deep learning approach for balance optimisation of patch panel assembly line-Reference-Cited by-同舟云学术

A deep learning approach for balance optimisation of patch panel assembly line

Published:2024-01-31 Issue: Volume: Page:1-19
ISSN:0954-4828
Container-title:Journal of Engineering Design
language:en
Short-container-title:Journal of Engineering Design

Author:

Wang Xinghe¹²,Lin Chaoquan²,Yang Shuo³,Chen Jiabin¹,Liu Bihua⁴,Chipusu Kavimbi⁵

Affiliation:

1. School of Intelligent Manufacturing Engineering, Liming Vocational University, Quanzhou, People’s Republic of China

2. Pusat Technology Pengajaran & Multimedia, Universiti Sains Malaysia, Pulau Pinang, Malaysia

3. Northwest University of Political Science and Law, Xi’an, People’s Republic of China

4. School of Design, Fujian University of Technology, Fuzhou, People’s Republic of China

5. Department of Traffic and Transportation Engineering, Central South University, Changsha Hunan, People’s Republic of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09544828.2024.2309861

Reference30 articles.

1. Implementing Industry 4.0 principles

2. A systematic literature review of machine learning methods applied to predictive maintenance

3. Chaki, S., Y. Zagayevskiy, X. Shi, T. Wong, and Noor, Z. 2020. “Machine Learning for Proxy Modeling of Dynamic Reservoir Systems: Deep Neural Network DNN and Recurrent Neural Network RNN Applications.” In International Petroleum Technology Conference. IPTC.

4. Computerized predetermined motion-time systems in manufacturing industries

5. ISOPAR, a new and improved sympolic optimizing assembly routine for the IBM 650

Cited by 2 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. Replicability and reproducibility of data-intensive design research using workflows - example in facial expression synchrony as a measure of empathy;Journal of Engineering Design;2024-09

2. Optimising logistics in intelligent transportation systems through CNN-IT: a convolutional neural network approach for enhanced efficiency and precision;Journal of Engineering Design;2024-03-24