Acoustic emission source location in unidirectional carbon-fiber-reinforced plastic plates with virtually trained artificial neural networks
Acoustic emission (AE) source location in a unidirectional carbon‐fiber‐reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined th...
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| Veröffentlicht in: | Journal of applied polymer science 2011-12, Vol.122 (6), p.3506-3513 |
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| container_title | Journal of applied polymer science |
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| creator | Caprino, G. Lopresto, V. Leone, C. Papa, I. |
| description | Acoustic emission (AE) source location in a unidirectional carbon‐fiber‐reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold‐crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave‐propagation theory, was able to accurately model the experimental trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011 |
| doi_str_mv | 10.1002/app.34758 |
| format | Article |
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The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold‐crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave‐propagation theory, was able to accurately model the experimental trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011</description><identifier>ISSN: 0021-8995</identifier><identifier>ISSN: 1097-4628</identifier><identifier>EISSN: 1097-4628</identifier><identifier>DOI: 10.1002/app.34758</identifier><identifier>CODEN: JAPNAB</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>Acoustic emission ; Applied sciences ; Artificial neural networks ; Carbon fiber reinforced plastics ; composites ; damage zone ; Exact sciences and technology ; fibers ; Forms of application and semi-finished materials ; Laminates ; Learning theory ; Materials science ; Mathematical models ; Neural networks ; plastics ; Plates ; Polymer industry, paints, wood ; Polymers ; Position (location) ; Technology of polymers</subject><ispartof>Journal of applied polymer science, 2011-12, Vol.122 (6), p.3506-3513</ispartof><rights>Copyright © 2011 Wiley Periodicals, Inc.</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4688-192be6ea426b94e54feb6ed9829ffd532dd033b13cb229798456e5ffae7f82073</citedby><cites>FETCH-LOGICAL-c4688-192be6ea426b94e54feb6ed9829ffd532dd033b13cb229798456e5ffae7f82073</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fapp.34758$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fapp.34758$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,1411,23910,23911,25119,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24623804$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Caprino, G.</creatorcontrib><creatorcontrib>Lopresto, V.</creatorcontrib><creatorcontrib>Leone, C.</creatorcontrib><creatorcontrib>Papa, I.</creatorcontrib><title>Acoustic emission source location in unidirectional carbon-fiber-reinforced plastic plates with virtually trained artificial neural networks</title><title>Journal of applied polymer science</title><addtitle>J. Appl. Polym. Sci</addtitle><description>Acoustic emission (AE) source location in a unidirectional carbon‐fiber‐reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold‐crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave‐propagation theory, was able to accurately model the experimental trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011</description><subject>Acoustic emission</subject><subject>Applied sciences</subject><subject>Artificial neural networks</subject><subject>Carbon fiber reinforced plastics</subject><subject>composites</subject><subject>damage zone</subject><subject>Exact sciences and technology</subject><subject>fibers</subject><subject>Forms of application and semi-finished materials</subject><subject>Laminates</subject><subject>Learning theory</subject><subject>Materials science</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>plastics</subject><subject>Plates</subject><subject>Polymer industry, paints, wood</subject><subject>Polymers</subject><subject>Position (location)</subject><subject>Technology of polymers</subject><issn>0021-8995</issn><issn>1097-4628</issn><issn>1097-4628</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqFkV1rFDEUhgdRcG298B8MiKAX0-Zj8nW5LLUttlpE8TJkMieYNp0Zk0y3-x_6o83u1l4I4tUh5HkfkvNW1RuMjjBC5NhM0xFtBZPPqgVGSjQtJ_J5tSh3uJFKsZfVq5SuEcKYIb6oHpZ2nFP2toZbn5IfhzqNc7RQh9GavD37oZ4H3_sIdns2obYmduPQON9BbCL4wY0l0ddTMDtVmRlSvfb5Z33nY55NCJs6R-OHQpmYvfPWF9EAc9yNvB7jTTqsXjgTErx-nAfV948n31ZnzcWX0_PV8qKxLZeywYp0wMG0hHeqBdY66Dj0ShLlXM8o6XtEaYep7QhRQsmWcWDOGRBOEiToQfV-753i-GuGlHX5u4UQzABlGxpzgSkigrX_RxEhkmOCUEHf_oVel02WfRWKYaGw4i0t1Ic9ZeOYUgSnp-hvTdwUld5WqEuFeldhYd89Gk2yJrhoBuvTU4CUbqlE20ce77m1D7D5t1Avr67-mJt9wqcM908JE280F1Qw_ePzqcZfP63OxCXViP4GDvG8Hg</recordid><startdate>20111215</startdate><enddate>20111215</enddate><creator>Caprino, G.</creator><creator>Lopresto, V.</creator><creator>Leone, C.</creator><creator>Papa, I.</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><general>Wiley</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>JG9</scope><scope>7QO</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20111215</creationdate><title>Acoustic emission source location in unidirectional carbon-fiber-reinforced plastic plates with virtually trained artificial neural networks</title><author>Caprino, G. ; Lopresto, V. ; Leone, C. ; Papa, I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4688-192be6ea426b94e54feb6ed9829ffd532dd033b13cb229798456e5ffae7f82073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Acoustic emission</topic><topic>Applied sciences</topic><topic>Artificial neural networks</topic><topic>Carbon fiber reinforced plastics</topic><topic>composites</topic><topic>damage zone</topic><topic>Exact sciences and technology</topic><topic>fibers</topic><topic>Forms of application and semi-finished materials</topic><topic>Laminates</topic><topic>Learning theory</topic><topic>Materials science</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>plastics</topic><topic>Plates</topic><topic>Polymer industry, paints, wood</topic><topic>Polymers</topic><topic>Position (location)</topic><topic>Technology of polymers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Caprino, G.</creatorcontrib><creatorcontrib>Lopresto, V.</creatorcontrib><creatorcontrib>Leone, C.</creatorcontrib><creatorcontrib>Papa, I.</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>Biotechnology Research Abstracts</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Journal of applied polymer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Caprino, G.</au><au>Lopresto, V.</au><au>Leone, C.</au><au>Papa, I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Acoustic emission source location in unidirectional carbon-fiber-reinforced plastic plates with virtually trained artificial neural networks</atitle><jtitle>Journal of applied polymer science</jtitle><addtitle>J. Appl. Polym. Sci</addtitle><date>2011-12-15</date><risdate>2011</risdate><volume>122</volume><issue>6</issue><spage>3506</spage><epage>3513</epage><pages>3506-3513</pages><issn>0021-8995</issn><issn>1097-4628</issn><eissn>1097-4628</eissn><coden>JAPNAB</coden><abstract>Acoustic emission (AE) source location in a unidirectional carbon‐fiber‐reinforced plastic plate was attempted with artificial neural network (ANN) technology. The AE events were produced by a lead break, and the response wave was received by piezoelectric sensors. The time of arrival, determined through the conventional threshold‐crossing technique, was used to measure the dependence of the wave velocity on the fiber orientation. A simple empirical formula, relying on classical lamination and suggested by wave‐propagation theory, was able to accurately model the experimental trend. On the basis of the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers and were adopted to select two potentially effective ANN architectures. For final validation, experimental tests were carried out, with the source positioned at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and those predicted by the virtually trained ANNs. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011</abstract><cop>Hoboken</cop><pub>Wiley Subscription Services, Inc., A Wiley Company</pub><doi>10.1002/app.34758</doi><tpages>8</tpages></addata></record> |
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| subjects | Acoustic emission Applied sciences Artificial neural networks Carbon fiber reinforced plastics composites damage zone Exact sciences and technology fibers Forms of application and semi-finished materials Laminates Learning theory Materials science Mathematical models Neural networks plastics Plates Polymer industry, paints, wood Polymers Position (location) Technology of polymers |
| title | Acoustic emission source location in unidirectional carbon-fiber-reinforced plastic plates with virtually trained artificial neural networks |
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