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<XML><RECORDS>
<RECORD>
	<REFERENCE_TYPE>0</REFERENCE_TYPE>
	<AUTHORS>
		<AUTHOR>Liu, Z.F.</AUTHOR>
		<AUTHOR>Liu, X.P.</AUTHOR>
		<AUTHOR>Wang, S.W.</AUTHOR>
		<AUTHOR>Liu, G.F.</AUTHOR>
	</AUTHORS>
	<YEAR>2002</YEAR>
	<TITLE>Recycling strategy and a recyclability assessment model based on an artificial neural network</TITLE>
	<SECONDARY_TITLE>Journal of Materials Processing Technology</SECONDARY_TITLE>
	<VOLUME>129</VOLUME>
	<PAGES>500-506</PAGES>
	<KEYWORDS>
		<KEYWORD>Recycling</KEYWORD>
		<KEYWORD>strategy,</KEYWORD>
		<KEYWORD>Product</KEYWORD>
		<KEYWORD>recycling,</KEYWORD>
		<KEYWORD>Artificial</KEYWORD>
		<KEYWORD>neural</KEYWORD>
		<KEYWORD>network,</KEYWORD>
		<KEYWORD>Assessment</KEYWORD>
		<KEYWORD>model,</KEYWORD>
		<KEYWORD>Design</KEYWORD>
		<KEYWORD>for</KEYWORD>
		<KEYWORD>recycling</KEYWORD>
	</KEYWORDS>
	<ABSTRACT>Designing products for recyclability is driven by environmental and economic goals. To obtain good recyclability, two measures can beadopted. One is better recycling strategy and technology; whilst the other is design for recycling (DFR). Recyclability assessment is one of themost important contents in DFR. First, this paper discusses the content of DFR, and strategies and types related to product recycling, andpoints out that whether recyclability is easy or difficult depends on the product design. Then the methods and the procedure of recyclabilityassessment based on artificial neural network are explored in detail. Finally, a case study shows that this method is simple and operable.</ABSTRACT>
</RECORD>
</RECORDS></XML>