Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data

In this work, the ultimate strength of aluminum/silicon carbide (Al/SiC) composites was predicted by using acoustic emission (AE) parameters through artificial neural network (ANN) analysis. With this aim, a series of fourteen Al/SiC tensile samples were loaded up to the failure to investigate the a...

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Main Authors:	Christopher Loo Mahil C., Sasikumar T., Santulli C., Fragassa C.
Format:	Article
Language:	English
Published:	University of Belgrade - Faculty of Mechanical Engineering, Belgrade 2018-01-01
Series:	FME Transactions
Subjects:	failure prediction acoustic emission felicity ratio riseangle feed forward neural network
Online Access:	https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2018/1451-20921802253C.pdf

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record_format	Article
spelling	doaj-6a3307973bee46ef8cf6d935851ab54f2020-11-25T03:53:30ZengUniversity of Belgrade - Faculty of Mechanical Engineering, BelgradeFME Transactions1451-20922406-128X2018-01-014622532581451-20921802253CNeural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle dataChristopher Loo Mahil C.0Sasikumar T.1Santulli C.2Fragassa C.3Sathyabama University, Department of Mechanical Engineering, IndiaLord Jegannath College of Engineering & Technology, Department of Mechanical Engineering, IndiaUniversità degli Studi di Camerino, School of Architecture and Design, ItalyAlma Mater Studiorum University of Bologna, Department of Industrial Engineering, ItalyIn this work, the ultimate strength of aluminum/silicon carbide (Al/SiC) composites was predicted by using acoustic emission (AE) parameters through artificial neural network (ANN) analysis. With this aim, a series of fourteen Al/SiC tensile samples were loaded up to the failure to investigate the amplitude distribution of AE events detected during loading. A back propagation ANN was prepared to correlate the amplitude values generated during loading up to 60% of known ultimate strength with ultimate failure strength of the samples. Three individual neural networks generated with parameters like hits, the Felicity ratio and rise angle were trained towards anticipating the ultimate strength value, which was predicted within the worst case error of -3.51 %, -4.73 %, and -2.73 %, respectively. The failure prediction accuracy by using rise angle as input was found to be slightly better, although the three neural networks all proved effective.https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2018/1451-20921802253C.pdffailure predictionacoustic emissionfelicity ratioriseanglefeed forward neural network
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Christopher Loo Mahil C. Sasikumar T. Santulli C. Fragassa C.
spellingShingle	Christopher Loo Mahil C. Sasikumar T. Santulli C. Fragassa C. Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data FME Transactions failure prediction acoustic emission felicity ratio riseangle feed forward neural network
author_facet	Christopher Loo Mahil C. Sasikumar T. Santulli C. Fragassa C.
author_sort	Christopher Loo Mahil C.
title	Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data
title_short	Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data
title_full	Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data
title_fullStr	Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data
title_full_unstemmed	Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data
title_sort	neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data
publisher	University of Belgrade - Faculty of Mechanical Engineering, Belgrade
series	FME Transactions
issn	1451-2092 2406-128X
publishDate	2018-01-01
description	In this work, the ultimate strength of aluminum/silicon carbide (Al/SiC) composites was predicted by using acoustic emission (AE) parameters through artificial neural network (ANN) analysis. With this aim, a series of fourteen Al/SiC tensile samples were loaded up to the failure to investigate the amplitude distribution of AE events detected during loading. A back propagation ANN was prepared to correlate the amplitude values generated during loading up to 60% of known ultimate strength with ultimate failure strength of the samples. Three individual neural networks generated with parameters like hits, the Felicity ratio and rise angle were trained towards anticipating the ultimate strength value, which was predicted within the worst case error of -3.51 %, -4.73 %, and -2.73 %, respectively. The failure prediction accuracy by using rise angle as input was found to be slightly better, although the three neural networks all proved effective.
topic	failure prediction acoustic emission felicity ratio riseangle feed forward neural network
url	https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2018/1451-20921802253C.pdf
work_keys_str_mv	AT christopherloomahilc neuralnetworkpredictionofaluminumsiliconcarbidetensilestrengthfromacousticemissionriseangledata AT sasikumart neuralnetworkpredictionofaluminumsiliconcarbidetensilestrengthfromacousticemissionriseangledata AT santullic neuralnetworkpredictionofaluminumsiliconcarbidetensilestrengthfromacousticemissionriseangledata AT fragassac neuralnetworkpredictionofaluminumsiliconcarbidetensilestrengthfromacousticemissionriseangledata
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Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data

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