Machine Learning-Based Predictive Maintenance Strategies for Nanocomposite Processing Equipment

EasyChair Preprint 14601

Abstract

Predictive maintenance is a crucial aspect of ensuring the reliability and efficiency of nanocomposite processing equipment. This study explores the application of machine learning-based predictive maintenance strategies for nanocomposite processing equipment. By leveraging machine learning algorithms and sensor data, this approach enables real-time monitoring and prediction of equipment failures, reducing downtime and increasing overall productivity. The study focuses on the development and implementation of predictive models using techniques such as regression, classification, and clustering. The results demonstrate improved accuracy in fault detection and prediction, enabling proactive maintenance scheduling and minimizing equipment failures. This research contributes to the optimization of nanocomposite processing equipment maintenance, enhancing the overall efficiency and sustainability of the manufacturing process.

Keyphrases: Nanocomposite Processing, Predictive Maintenance, machine learning

@booklet{EasyChair:14601,
  author    = {Abi Cit},
  title     = {Machine Learning-Based Predictive Maintenance Strategies for Nanocomposite Processing Equipment},
  howpublished = {EasyChair Preprint 14601},
  year      = {EasyChair, 2024}}