NDLI: Experimental study of burn classification and prediction using indirect method in surface grinding of AISI 1045 steel

Content Provider	Springer Nature Link
Author	Yang, Zhensheng Yu, Zhonghua
Copyright Year	2013
Abstract	Grinding burn is a discoloration phenomenon according to the thickness of oxide layer on the ground surface. This study tries to establish an automatic grinding burn detection system with robust burn features that are caused by burn and not by the design parameters. To address this issue, a method based on acoustic emission sensor, accelerator, electric current transducers, and voltage transducers was proposed in an attempt to extract burn signatures. A trial-and-error experimental procedure was presented to find out burn threshold. Vitrified aluminum oxide grinding wheel and AISI 1045 steel workpiece were used in the grinding test, as they were the most commonly used wheel–workpiece combinations in conventional grinding process. With the help of fast Fourier transform and discrete wavelet transform, the spectral centroid of AE signal, the maximum value of power signal, and the RMS of the AE wavelet decomposition transform from wavelet decomposition levels d1 to d5 were extracted as burn features. The spectral centroid of AE signal was believed not to be affected by grinding parameters. A classification and prediction system based on support vector machine was established in order to identify grinding burn automatically. Results indicate that the classification system performs quite well on grinding burn classification and prediction.
Starting Page	2439
Ending Page	2449
Page Count	11
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	68
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2013-03-13
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Grinding burn Sensor Feature extraction FFT Discrete wavelet transform Support vector machine Industrial and Production Engineering Production/Logistics/Supply Chain Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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