Development of grinding intelligent monitoring and big data-driven decision making expert system towards high efficiency and low energy consumption: experimental approach-Reference-Cited by-同舟云学术

Development of grinding intelligent monitoring and big data-driven decision making expert system towards high efficiency and low energy consumption: experimental approach

Published:2023-02-26 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Wang Jinling,Tian Yebing^ORCID,Hu Xintao,Fan Zenghua,Han Jinguo,Liu Yanhou

Funder

National Natural Science Foundation of China

Taishan Scholar Foundation of Shandong Province

Shandong Provincial Key Research & Development Project

Natural Science Foundation of Shandong Province

Scientific Innovation Project for Young Scientists in Shandong Provincial Universities

Key Research & Development Project of Zibo

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-023-02089-1.pdf

Reference46 articles.

1. Aghbashlo, M., Peng, W. X., Tabatabaei, M., Kalogirou, S. A., Soltanian, S., Hosseinzadeh-Bandbafha, H., Mahian, O., & Lam, S. S. (2021). Machine learning technology in biodiesel research: A review. Progress in Energy and Combustion Science, 85, 1–112. https://doi.org/10.1016/j.pecs.2021.100904

2. Alajmi, M. S., Alfares, F. S., & Alfares, M. S. (2019). Selection of optimal conditions in the surface grinding process using the quantum based optimisation method. Journal of Intelligent Manufacturing, 30, 1469–1481. https://doi.org/10.1007/s10845-017-1326-2

3. Arun, A., Rameshkumar, K., Unnikrishnan, D., & Sumesh, A. (2018). Tool condition monitoring of cylindrical grinding process using acoustic emission sensor. Materials Today: Proceedings, 5, 11888–11899. https://doi.org/10.1016/j.precisioneng.2018.09.018

4. Bracke, S., Radetzky, M., Rosebrock, C., & Ulutas, B. (2019). Efficiency and effectivity of high precision grinding manufacturing processes: An approach based on combined DEA and cluster analyses. Procedia CIRP, 79, 292–297. https://doi.org/10.1016/j.procir.2019.02.069

5. Brinksmeier, E., Klocke, F., Lucca, D. A., Solter, J., & Meyer, D. (2014). Process signatures—A new approach to solve the inverse surface integrity problem in machining processes. Procedia CIRP, 13, 429–434. https://doi.org/10.1016/j.procir.2014.04.073

Cited by 5 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. Developing Expert Systems for Improving Energy Efficiency in Manufacturing: A Case Study on Parts Cleaning;Energies;2024-07-11

2. Surface Integrity Evolution in Grinding by means of In-Process Eddy Current Inspections;Procedia CIRP;2024

3. Artificial Intelligence Approaches for Predictive Power Consumption Modeling in Machining-Short Review;E3S Web of Conferences;2024

4. A modified RBF-CBR model considering evaluation index for gear grinding process with worm grinding wheel decision support system;Journal of Intelligent Manufacturing;2023-06-20

5. Online prediction of grinding wheel condition and surface roughness for the fused silica ceramic composite material based on the monitored power signal;Journal of Materials Research and Technology;2023-05