Machine Learning for Real-Time Monitoring and Control of Nanocomposite Production Processes

EasyChair Preprint 14668

Abstract

The production of nanocomposites is a complex process that requires precise monitoring and control to ensure the quality and consistency of the final product. Machine learning (ML) techniques offer a promising solution for real-time monitoring and control of nanocomposite production processes. This paper presents a framework for the application of ML algorithms in the real-time monitoring and control of nanocomposite production processes. The framework utilizes sensor data and ML models to predict and prevent defects, optimize process parameters, and improve product quality. The results show that the ML-based approach can significantly improve the accuracy and efficiency of the monitoring and control process, leading to reduced waste, improved product quality, and increased productivity. The paper demonstrates the potential of ML for real-time monitoring and control of nanocomposite production processes and highlights the benefits of integrating ML into industrial manufacturing processes.

Keyphrases: Nanocomposite production, machine learning, real-time monitoring

@booklet{EasyChair:14668,
  author    = {Abi Cit},
  title     = {Machine Learning for Real-Time Monitoring and Control of Nanocomposite Production Processes},
  howpublished = {EasyChair Preprint 14668},
  year      = {EasyChair, 2024}}