Design and Optimization of CNC Milling Process Based on Vibration System Detector Using Particle Swarm Optimization Algorithm Method

DOI

10.2991/978-94-6463-364-1_95How to use a DOI?

Keywords

CNC Milling; vibration detector; PSO; BPANN-GA

Abstract

Machine maintenance in industry requires speed and convenience, one method is vibration analysis. Engine vibrations cause a pattern of sound emitted by the engine, where the sound of one engine mixes with the sound of another engine. Excessive vibration levels in the engine indicate damage to engine components. If this excessive vibration is not acted upon, the machine will experience more serious damage. In order for it to work optimally, the machine requires maintenance or maintenance. Machine maintenance systems are very important in industry to extend machine life. One maintenance method that is often used is predictive maintenance based on vibration signals. Predictive maintenance is a type of maintenance that can be carried out by monitoring the vibration conditions caused by the machine. One way that can be done to overcome damage to the machine is to analyze the vibration level in the machine in the form of the amplitude value of the vibration speed. This method can predict machine damage based on the vibration signals that arise, so that serious damage can be avoided. The research designed and made a CNC Milling machine prototype which is a combination of two outputs of vibration detection and process optimization. The aim of this research is to find out and determine parameter settings that are able to produce an optimum response. The experimental design used is full factorial, orthogonal array, and response surface methodology, with the optimization methods being back propagation neural network (BP ANN) and particle swarm optimization (PSO) algorithm.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chairul Anam
AU  - Abdul Rohman
AU  - Eka Mistiko Rini
AU  - Mahros Darsin
PY  - 2024
DA  - 2024/02/17
TI  - Design and Optimization of CNC Milling Process Based on Vibration System Detector Using Particle Swarm Optimization Algorithm Method
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
PB  - Atlantis Press
SP  - 1044
EP  - 1057
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-364-1_95
DO  - 10.2991/978-94-6463-364-1_95
ID  - Anam2024
ER  -