A Quantum-assisted Diagnostics Method for Intelligent Manufacturing-Reference-Cited by-同舟云学术

A Quantum-assisted Diagnostics Method for Intelligent Manufacturing

Published:2023-12-14 Issue: Volume: Page:1-8
ISSN:
Container-title:Artificial Intelligence and Multimedia Data Engineering
language:
Short-container-title:

Author:

Sharma Vishal¹

Affiliation:

1. Department of Computer Science and Engineering, WILP Faculty, BITS Pilani, Jhunjhunu, Rajasthan, India

Abstract

 Present manufacturing machines have few methods to investigate machine health. To minimize issues and enhance the correctness of machine decisions and automation, machine health conditions require to be investigated. Therefore, the evolution of a fresh investigating and diagnostics approach for additive manufacturing machines is needed for better productivity in Industry 4.0. In the current chapter, an intelligent technique for the condition monitoring of additive manufacturing (AM) is described, where an accelerometer fitted on the extruder assembly is used to receive vibration signals. The process errors with the printer were the worn-out timing belts driving the extruder assembly. Quantum-based Support Vector Machine was simulated to identify the 3D-printer status. The simulation outcomes presented here show that this approach has better correctness as compared to the previous Support Vector Machine techniques.<br>

Publisher

BENTHAM SCIENCE PUBLISHERS

Reference26 articles.

1. Xiong J.; Zhang Q.; Peng Z.; Sun G.; Xu W.; Wang Q.; A diagnosis method for rotation machinery faults based on dimensionless indexes combined with-nearest neighbor algorithm. Mathematical Problems in Engineering, Hindawi 2015

2. Zhang Q.H.; Fault diagnosis in unit based on artificial immune detectors system. China Petrochemical Press 2008

3. Sharma V.; Banerjee S.; Quantum communication using code division multiple access network. Opt Quantum Electron 2020 ,52(8),381

4. Qiu G.; Tang X.; Zhuang L.; Yang Z.; Application of neural network trained by chaos particle swarm optimization to fault diagnosis for rotating machinery. Zhongguo Jixie Gongcheng 2008 ,19(21),2642-2645

5. Dou D.Y.; Zhao Y.K.; A priority and diagnosis tree-based expert system for fault diagnosis of rotating machinery. Zhongguo Dianji Gongcheng Xuebao 2008 ,28(32),82-89