Title :
A comparative study of signal and image processing systems for condition monitoring of milling processes using artificial intelligence
Author :
Elgargni, Milad Ahmed ; Al-Habaibeh, Amin
Author_Institution :
Adv. Design & Manuf. Eng. Centre, Nottingham Trent Univ., Nottingham, UK
Abstract :
A comparative study between two types of tool wear monitoring systems for milling processes is introduced in this paper. The suggested sensory fusion approach includes the implementation of an infrared camera, in addition to force, vibration, sound and acoustic emission sensors. The majority of the research work available in literature and industry focuses on using one dimensional signals, such as force, vibration. Two dimensional data, such as infrared and visual images, are limited in literature in relation to machining operations. This work compares between one dimensional and two dimensional data for the development of a tool condition monitoring system for milling processes. The paper presents a comparative study between the performance of signal and image processing algorithms using neural networks. Fourier Transformation and Wavelets analysis are used to process one dimensional and Two dimensional data respectively. The results indicate that two dimensional data obtained from infrared images has significant capability in comparison to one dimensional data for the detection of tool wear for the selected image and signal processing algorithms.
Keywords :
Fourier transforms; acoustic emission; artificial intelligence; condition monitoring; force sensors; image processing; infrared detectors; machine tools; milling; neural nets; production engineering computing; sensor fusion; vibrations; wavelet transforms; wear; Fourier transformation; acoustic emission sensors; artificial intelligence; force sensors; image processing systems; infrared camera; infrared images; machining operations; milling process; neural networks; sensory fusion approach; signal processing systems; sound sensors; tool condition monitoring system; tool wear detection; tool wear monitoring systems; vibration sensors; visual images; wavelet analysis; Artificial neural networks; Cameras; Force; Image processing; Milling; Sensors; Infrared; Sensor fusion; Signal/image processing; tool wear;
Conference_Titel :
Applied Electrical Engineering and Computing Technologies (AEECT), 2013 IEEE Jordan Conference on
Conference_Location :
Amman
Print_ISBN :
978-1-4799-2305-2
DOI :
10.1109/AEECT.2013.6716474