Acta Scientific Computer Sciences

Short Communication Volume 4 Issue 7

Predictive Maintenance for Injection Molding Industry

Radhya Sahal*

Faculty of Computer Science and Engineering, Hodeidah University, Al Hudaydah, Yemen

*Corresponding Author: Radhya Sahal, Faculty of Computer Science and Engineering, Hodeidah University, Al Hudaydah, Yemen.

Received: April 15, 2022; Published: June 17, 2022

Plastic injection molding is the third largest sector in manufacturing which warrants special consideration in the data Industrial Internet of Things as it impacts manufacturers and their customers. For instance, the plastic injection molding smart factory can efficiently use data for every process to begin preheating only when it has received data that the previous process is nearing completion.


Keywords: Molding Industry; Predictive Maintenance; Injection Molding; Plastic

References

  1. R Sahal., et al. "Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case”. Journal of Manufacturing Systems 54 (2020): 138-151.
  2. H Lee., et al. "A framework of a smart injection molding system based on real-time data”. Procedia Manufacturing 11 (2017): 1004-1011.
  3. A Librantz., et al. "Artificial intelligence based system to improve the inspection of plastic mould surfaces”. Journal of Intelligent Manufacturing 28 (2017): 181-190.
  4. A Tellaeche and R Arana. "Machine learning algorithms for quality control in plastic molding industry”. in 2013 IEEE 18th Conference on Emerging Technologies and Factory Automation (ETFA) (2013): 1-4.

Citation

Citation: Radhya Sahal. “Predictive Maintenance for Injection Molding Industry". Acta Scientific Computer Sciences 4.7 (2022): 30-31.

Copyright

Copyright: © 2022 Radhya Sahal. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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