Impact damage detection and quantification in CFRP laminates; a precursor to machine learning

M.T.H. Sultan; K. Worden; W.J. Staszewski; Janice.M. Dulieu-Barton; A. Hodzic

Impact damage detection and quantification in CFRP laminates; a precursor to machine learning

M.T.H. Sultan, K. Worden, W.J. Staszewski, Janice.M. Dulieu-Barton, A. Hodzic

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

The main objective of this research is to detect and classify impact damage in structures made from composite materials. The material chosen for this research is a Carbon Fiber Reinforced Polymer (CFRP) composite with a MTM57 epoxy resin system. This material was fabricated to produce laminated plate specimens of 250 mm $ 150 mm, each with three PZT sensors placed at different points in order to record the responses from impact events. An impact hammer was used to produce FRF and time data corresponding to undamaging impacts. To perform the damaging impact tests, an instrumented drop test machine was used and the impact energy was set to range from 2.6J to 41.72J. The signals captured from each specimen were recorded in a data acquisition system for evaluation and the impacted specimens were X-rayed to evaluate the damage areas. As a precursor to the application of machine learning, a number of univariate features for damage identification were investigated.

Original language	English
Title of host publication	7th International Workshop on Structural Health Monitoring (11/09/09)
Publication status	Published - 2009

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title = "Impact damage detection and quantification in CFRP laminates; a precursor to machine learning",

abstract = "The main objective of this research is to detect and classify impact damage in structures made from composite materials. The material chosen for this research is a Carbon Fiber Reinforced Polymer (CFRP) composite with a MTM57 epoxy resin system. This material was fabricated to produce laminated plate specimens of 250 mm $ 150 mm, each with three PZT sensors placed at different points in order to record the responses from impact events. An impact hammer was used to produce FRF and time data corresponding to undamaging impacts. To perform the damaging impact tests, an instrumented drop test machine was used and the impact energy was set to range from 2.6J to 41.72J. The signals captured from each specimen were recorded in a data acquisition system for evaluation and the impacted specimens were X-rayed to evaluate the damage areas. As a precursor to the application of machine learning, a number of univariate features for damage identification were investigated.",

author = "M.T.H. Sultan and K. Worden and W.J. Staszewski and Janice.M. Dulieu-Barton and A. Hodzic",

year = "2009",

language = "English",

booktitle = "7th International Workshop on Structural Health Monitoring (11/09/09)",

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Impact damage detection and quantification in CFRP laminates; a precursor to machine learning. / Sultan, M.T.H.; Worden, K.; Staszewski, W.J.; Dulieu-Barton, Janice.M.; Hodzic, A.

7th International Workshop on Structural Health Monitoring (11/09/09). 2009.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Impact damage detection and quantification in CFRP laminates; a precursor to machine learning

AU - Sultan, M.T.H.

AU - Worden, K.

AU - Staszewski, W.J.

AU - Dulieu-Barton, Janice.M.

AU - Hodzic, A.

PY - 2009

Y1 - 2009

N2 - The main objective of this research is to detect and classify impact damage in structures made from composite materials. The material chosen for this research is a Carbon Fiber Reinforced Polymer (CFRP) composite with a MTM57 epoxy resin system. This material was fabricated to produce laminated plate specimens of 250 mm $ 150 mm, each with three PZT sensors placed at different points in order to record the responses from impact events. An impact hammer was used to produce FRF and time data corresponding to undamaging impacts. To perform the damaging impact tests, an instrumented drop test machine was used and the impact energy was set to range from 2.6J to 41.72J. The signals captured from each specimen were recorded in a data acquisition system for evaluation and the impacted specimens were X-rayed to evaluate the damage areas. As a precursor to the application of machine learning, a number of univariate features for damage identification were investigated.

AB - The main objective of this research is to detect and classify impact damage in structures made from composite materials. The material chosen for this research is a Carbon Fiber Reinforced Polymer (CFRP) composite with a MTM57 epoxy resin system. This material was fabricated to produce laminated plate specimens of 250 mm $ 150 mm, each with three PZT sensors placed at different points in order to record the responses from impact events. An impact hammer was used to produce FRF and time data corresponding to undamaging impacts. To perform the damaging impact tests, an instrumented drop test machine was used and the impact energy was set to range from 2.6J to 41.72J. The signals captured from each specimen were recorded in a data acquisition system for evaluation and the impacted specimens were X-rayed to evaluate the damage areas. As a precursor to the application of machine learning, a number of univariate features for damage identification were investigated.

M3 - Conference Contribution (Conference Proceeding)

BT - 7th International Workshop on Structural Health Monitoring (11/09/09)

ER -