<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="https://www.zbc.uz.zgora.pl/repozytorium/style/common/xsl/oai-style.xsl"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" 
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/
         http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
	<responseDate>2026-06-28T05:02:13Z</responseDate>
	<request identifier="oai:zbc.uz.zgora.pl:82258" metadataPrefix="oai_dc" verb="GetRecord">
	https://zbc.uz.zgora.pl/repozytorium/oai-pmh-repository.xml</request>
	<GetRecord>
	
  <record>
	<header>
		<identifier>oai:zbc.uz.zgora.pl:82258</identifier>
	    <datestamp>2024-12-16T11:41:20Z</datestamp>
		  <setSpec>DigitalLibraryZielonaGora</setSpec> 	      <setSpec>DigitalLibraryZielonaGora:Repository</setSpec> 	      <setSpec>DigitalLibraryZielonaGora:Repository:ListJurnals:IJAME</setSpec> 	      <setSpec>DigitalLibraryZielonaGora:Repository:Faculties</setSpec> 	      <setSpec>DigitalLibraryZielonaGora:Repository:ListJurnals:IJAME:IJAME24</setSpec> 	      <setSpec>DigitalLibraryZielonaGora:Repository:Faculties:FacultyMechanical</setSpec> 	      <setSpec>DigitalLibraryZielonaGora:Repository:TypesWork</setSpec> 	      <setSpec>DigitalLibraryZielonaGora:Repository:TypesWork:Articles</setSpec> 	      <setSpec>DigitalLibraryZielonaGora:Repository:ListJurnals</setSpec> 	    </header>
		<metadata>
	<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title xml:lang="pl"><![CDATA[Optimisation of material composition in functionally graded plates using a structure-tuned deep neural network]]></dc:title>
<dc:creator><![CDATA[Chiba, Ryoichi]]></dc:creator>
<dc:creator><![CDATA[Kishida, Takuya]]></dc:creator>
<dc:creator><![CDATA[Seki, Ryoto]]></dc:creator>
<dc:creator><![CDATA[Sato, Seiya]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[neural networks]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[optimal design]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[functionally graded material]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[thermal stresses]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[material design]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[multi-layered material]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[This study presents a neural network (NN)-based approach for optimising material composition in multi-layered functionally graded (FG) plates to minimise steady-state thermal stress. The focus is on the metal-ceramic composition across the thickness of heat-resistant FG plates, with the volume fractions of the ceramic constituent in each layer treated as design variables. A fully-connected NN, configured with an open-source Bayesian optimisation framework, is employed to predict the maximum in-plane thermal stress for various combinations of design variables.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The optimal distribution of material composition is determined by applying a backpropagation algorithm to the NN. Numerical computations on ten- and twelve-layered FG plates demonstrate the usefulness of this approach in designing FG materials. NNs trained with 8000 samples enable the successful acquisition of valid optimal solutions within a practical timeframe.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Jurczak, Paweł - red.]]></dc:contributor>
<dc:date><![CDATA[2024]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:format xml:lang="pl"><![CDATA[application/pdf]]></dc:format>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/82258/Volume29_Issue4_paper_06.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/91610/edition/82258/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:82258]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[IJAME, volume 29, number 4 (2024)]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:91610]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
<dc:rights xml:lang="pl"><![CDATA[CC 4.0]]></dc:rights>
</oai_dc:dc>

</metadata>
	  </record>	</GetRecord>
</OAI-PMH>
